FEATURED RECOGNITIONS
Utah Public Health Data Modernization: Scalable Architecture Proof of Concept 2026
CDC Foundation Workforce Acceleration Initiative (WAI)
What This Was About
As part of the CDC Foundation’s Workforce Acceleration Initiative (WAI), I presented a working proof of concept (POC) of the scalable data architecture and automated analytics platform developed for Bear River Health Department (BRHD) to multiple local health departments in Utah.
The objective was to demonstrate how a health department can modernize fragmented and manual data workflows using a low-cost, modular, and replicable architecture rather than relying on expensive enterprise technology or replacing its existing systems.
I also traveled to Tooele County to demonstrate the POC directly to public health leadership and teams, showcasing not only the concept but the actual working pipelines, automation, analytics, architecture, and operating costs developed through the BRHD implementation.
Presentation Highlights
The demonstration showcased an end-to-end architecture built using Apache NiFi, Google Cloud Storage, Google BigQuery, Metabase, Python, and automated email workflows.
Key capabilities demonstrated included:
-
Automated ingestion, validation, transformation, and loading of public health data
-
Cloud-based storage and analytics through Google Cloud and BigQuery
-
Apache NiFi workflows for orchestrating and automating data movement
-
Metabase dashboards for centralized KPI and program analytics
-
Automated email notifications throughout different stages of the data workflow
-
Data-quality checks and processing controls before information reaches analytics
-
A simple Google Drive folder-based entry point for non-technical users when source systems only provide manual exports
-
Support for both API-enabled systems and systems that require manual file exports
-
Modular components that can be connected to different datasets and programs, creating a plug-and-play modernization framework
The architecture was demonstrated as an automation layer that can work around existing health department systems rather than requiring organizations to replace them.
Demonstrating the Real Cost Advantage
An important part of the presentation was showing the actual cost of operating the BRHD architecture.
The implementation demonstrated that a modern cloud analytics environment could operate at approximately $220 per month, providing data pipelines, cloud storage, data warehousing, dashboards, and automation capabilities at a fraction of the cost typically associated with larger proprietary enterprise technology environments.
This was particularly important for rural and resource-constrained health departments, where technology budgets and technical staffing can make traditional modernization approaches difficult to sustain.
The presentation therefore focused not simply on what the technology could do, but on how health departments could realistically afford, operate, and replicate it.
Real-World Public Health Demonstration
The POC was demonstrated using Diabetes Prevention Program data, including the KPI:
“% of Diabetes Prevention Class participants achieving 4–7% weight loss.”
This provided a real public health use case for showing the complete journey from source data to automated processing and ultimately to decision-ready KPI analytics.
Seeing the automated analytics working on an actual public health program helped demonstrate that the architecture was not limited to a theoretical technology exercise. The same framework can be connected to different public health programs and data sources.
From BRHD to Multiple Utah Health Departments
The significance of the engagement extended beyond presenting the BRHD solution.
The demonstrations generated strong interest, appreciation, and engagement from health directors and public health teams from Tooele, Southeast Utah, Central Utah, and Bear River Health Department.
Following the presentations, health department teams began discussing common data sources that could potentially be used across departments, including EpiTrax and USIIS, creating an opportunity to explore how a shared modernization approach could support multiple jurisdictions.
This represented an important shift from building a solution for one health department toward considering how the architecture could become a repeatable model for multiple local health departments in Utah.
Impact and Potential Across Utah
The Utah engagement demonstrated that public health modernization can be approached through shared architecture and reusable components rather than isolated technology projects.
A health department could connect an API-enabled source directly, while another department using manual exports could simply place files into a designated Drive folder. The same underlying automation framework can then process, validate, warehouse, and visualize the information.
This flexibility makes the approach particularly relevant to Utah's diverse local health departments, which may operate with different systems, resources, workflows, and technical capabilities.
The POC therefore demonstrated a practical pathway toward standardizing automation and analytics while allowing each health department to retain its existing data sources and operational processes.
National Scalability & Long-Term Vision
Although the initial proof of concept was developed for BRHD and demonstrated within Utah, the underlying architecture was intentionally designed with replication beyond a single department or state in mind.
The same modular framework can potentially support different public health programs, datasets, jurisdictions, and ingestion methods. As additional departments identify common sources and requirements, reusable pipeline modules, KPI definitions, validation rules, and dashboard structures could reduce the effort required to modernize each new environment.
This creates a potential model for broader public health modernization in which low-cost cloud infrastructure, open-source technologies, automation, and standardized analytics can be adapted by health departments nationally, particularly organizations that cannot afford large enterprise technology investments.
Recognition & Professional Impact
Presenting the working BRHD POC directly to multiple health departments provided an opportunity for public health leaders to evaluate the architecture based on a real implementation, real costs, real automation, and real analytics outcomes.
The positive response and appreciation from multiple health directors and health department teams elevated the work beyond my individual project responsibilities and demonstrated interest in the approach from organizations outside my immediate project environment.
This engagement strengthened my role not only as the engineer who built the BRHD data platform, but as a contributor capable of demonstrating, explaining, and translating a technical modernization model for broader public health adoption.
It also provided an important bridge between implementation and influence: a solution initially developed for one health department became a working proof of concept presented to multiple public health organizations as a potential model for replication and modernization.
Featured in CDC Foundation's Workforce Acceleration Initiative (WAI) Newsletter
The work was featured in the CDC Foundation's internal Workforce Acceleration Initiative (WAI) newsletter under the title “Venkata Namuduri: Transforming Paper to Insights.” The feature highlighted my contributions to public health data modernization at Bear River Health Department, including the development of an automated data processing platform, the implementation of the "magic folder" workflow, and the demonstration of a scalable, low-cost proof of concept to multiple Utah health departments.
This feature provided organizational recognition of the work and documented its broader impact and potential for replication across local public health departments.
Since this is an internal WAI newsletter, the original newsletter link has been refreshed. Click below to view the offline version of the newsletter.
Click here to view the WAI Newsletter (Offline PDF Version)
_heic.png)
Official Speaker and Presenter — NACCHO 360 & PHI*con 2026
CDC Foundation | Workforce Acceleration Initiative (WAI)
I was selected as an official speaker and presenter at NACCHO 360 and PHI*con 2026, where I represented the CDC Foundation and Bear River Health Department by presenting "Data Modernization on a Budget: Building a Secure, Affordable, Response-Ready Analytics Platform for Local Health Departments." The presentation showcased how the Bear River Health Department transformed fragmented public health data systems into a secure, scalable, cloud-native analytics platform using modern open-source technologies, providing a practical model that other public health agencies can adopt to accelerate their own data modernization initiatives.
Presentation Highlights
During the session, I shared the complete end-to-end modernization journey—from legacy, spreadsheet-driven reporting processes to a fully automated cloud analytics platform supporting multiple public health programs.
Key highlights included:
-
Presented a secure, HIPAA-aligned analytics architecture built using Apache NiFi, Google Cloud Storage, Google BigQuery, Metabase, and OpenMetadata, demonstrating how modern cloud technologies can be successfully implemented within local public health agencies.
-
Demonstrated end-to-end automated ETL pipelines that ingest, validate, transform, and integrate data from multiple public health systems into a centralized cloud data warehouse, significantly reducing manual reporting efforts while improving data quality and consistency.
-
Showcased a scalable and cost-effective modernization framework designed specifically for rural and resource-constrained health departments, enabling enterprise-level analytics without the high costs typically associated with proprietary solutions.
-
Explained the importance of data governance through metadata management, lineage tracking, standardized KPI definitions, automated validation, role-based security, and HIPAA-compliant architecture to improve trust, transparency, and long-term maintainability.
-
Demonstrated interactive executive dashboards that provide leadership with near real-time visibility into organizational performance, enabling faster operational planning, grant reporting, strategic decision-making, and community health monitoring.
-
Introduced a proof-of-concept for Agentic AI-powered data agents, illustrating how artificial intelligence can enable natural language interaction with public health data, automate report generation, summarize complex datasets, and empower non-technical users with self-service analytics capabilities.
-
Shared practical implementation lessons, architectural decisions, technical challenges, and best practices learned throughout the project, providing attendees with a realistic roadmap for planning and executing similar modernization initiatives within their own organizations.
-
Discussed how combining modern data engineering, cloud-native architecture, open-source technologies, and emerging AI capabilities can create sustainable, future-ready public health data ecosystems that continue delivering value beyond the initial implementation.
-
Engaged with public health executives, informatics professionals, epidemiologists, technology leaders, and health department representatives from across the United States, exchanging ideas on scalable modernization strategies and future collaboration opportunities.
National Impact
The presentation demonstrated how modern, open-source data engineering can help public health agencies build secure, scalable, interoperable, and cost-effective analytics platforms without relying on expensive proprietary software. By presenting a real-world implementation at the Bear River Health Department, attendees were able to see how automated data pipelines, cloud-native infrastructure, and strong data governance practices can transform fragmented reporting processes into an integrated analytics ecosystem that supports faster, more informed decision-making.
Beyond the technical architecture, the session emphasized the importance of designing solutions that are practical, affordable, and sustainable for local health departments with limited budgets and technical resources. The modernization approach demonstrated that enterprise-grade analytics capabilities can be achieved using open-source technologies while maintaining security, compliance, scalability, and long-term operational efficiency.
The presentation also introduced emerging concepts around Agentic AI, highlighting how AI-powered data agents can further modernize public health by enabling natural language data access, intelligent report generation, automated insight discovery, and self-service analytics for non-technical users. These concepts generated discussions about the future role of AI in supporting public health operations, data accessibility, and evidence-based decision-making.
The session contributed to national conversations on public health data modernization by sharing a practical, field-tested implementation model that other organizations can adapt to their own environments. It also provided an opportunity to exchange knowledge with public health leaders, data professionals, and technology experts who are working to strengthen data infrastructure and digital transformation initiatives across the United States.
Click below to see more.
1: linkedin
2: Pulse
3: Naccho
.png)
.png)
.png)
___________________________________________________________________________________
WAI Changemaker Award — National Public Health Conference
I was honored to receive the Changemaker Award at the CDC Foundation’s Workforce Acceleration Initiative (WAI) Annual Summit, a national-level recognition for impactful contributions to public health data modernization.
This award highlights my work in transforming the Bear River Health Department’s data ecosystem from fragmented, manual, CSV-based workflows into a scalable, automated, and real-time analytics platform.
My Role & Impact:
As a Data Engineer under the CDC Foundation’s WAI program, I partnered closely with public health leadership to design and deliver an end-to-end data transformation strategy.
-
Architected and maintained a scalable data warehouse powering a unified KPI ecosystem across multiple public health programs
-
Built automated, low-cost cloud data pipelines (leveraging BigQuery, Apache NiFi, and open-source BI tools) to eliminate manual reporting inefficiencies
-
Designed and delivered 50+ KPI interactive dashboards, converting static CSV datasets into near real-time, decision-ready insights
-
Led user-centered design sessions with clinical, behavioral health, and environmental teams to ensure dashboards were intuitive, accessible, and actionable
-
Improved data accessibility and usability through simplified visuals, clear labeling, and optimized layouts tailored for non-technical users
-
Reduced enterprise analytics costs from tens of thousands annually to under $220/month, enabling replication across rural and budget-constrained health departments
-
Enabled leadership to make faster, data-driven decisions, strengthening policy communication, grant justification, and community health interventions
Innovation & Emerging Technology:
Recognized for my innovative and creative approach, I also initiated a proof-of-concept for Agentic AI-driven data agents, designed to:
-
Allow non-technical public health users to interact with data using natural language
-
Automate data retrieval, summarization, and insight generation
-
Provide flexible, self-service analytics capabilities without dependency on technical teams
-
Lay the foundation for AI-assisted decision-making in public health environments
This forward-looking initiative demonstrates how AI + data engineering can further democratize access to critical health insights.
Recognition Details:
The award was presented by Shannon Woods (Project Manager / Supervisor) and Michelle Panneton (Director) at the national WAI conference.
___________________________________________________________________________________
Scientific Publication & Research
Scientific Publication: Autonomous Data Governance in Artificial Intelligence Ecosystems: Architectures, Policies, and Control Mechanisms
Europub Journal of Education Research, Volume 7, Issue 2, 2026, Pages 1–21
I authored and published the scientific article “Autonomous Data Governance in Artificial Intelligence Ecosystems: Architectures, Policies, and Control Mechanisms,” examining the technical, organizational, and regulatory foundations required to govern data responsibly within artificial intelligence ecosystems. The research explores how AI-driven architectures can support automated governance processes while preserving institutional accountability, transparency, and human oversight.
The article examines how established frameworks, including the NIST Artificial Intelligence Risk Management Framework (AI RMF), ISO/IEC 42001:2023, and applicable provisions of the European Union AI Act, can inform the design of responsible data governance mechanisms. It explores policy automation, authorization controls, regulatory compliance, auditability, and traceability throughout the data lifecycle.
A central contribution of the research is a proposed conceptual architecture for supervised autonomous data governance, comprising five functional components: institutional authority, a policy repository, request evaluation, controlled execution, and audit logging. The proposed model emphasizes the distinction between automating administrative procedures and transferring decision-making authority to computational systems, ensuring that automated operations remain subject to established policies and appropriate human supervision.
The article was published in the Europub Journal of Education Research, Volume 7, Issue 2, 2026, and spans 21 pages. The publication identifies me as the author and documents the research as a theoretical and conceptual contribution based on bibliographic and documentary analysis.
Why this recognition matters:
-
Demonstrates scholarly contribution to the intersection of artificial intelligence, data governance, policy automation, and algorithmic accountability.
-
Establishes research experience in developing conceptual architectures for responsible and supervised AI-driven governance.
-
Connects my expertise in data engineering and healthcare data systems with broader questions of regulatory compliance, data protection, and trustworthy AI.
-
Demonstrates the ability to analyze established technical and regulatory frameworks and translate them into structured architectural proposals.
-
Extends my professional contributions beyond data engineering implementation into scientific research on responsible AI infrastructure and governance.
Publication:
Read the Scientific Article
DOI: 10.54745/ejerv7n2-005— the publication certificate records this DOI.
Scientific Publication: Building Intelligent Data Quality Agents Using Large Language Models
International Integralize Scientific Journal: Technology Section, Vol. 6, No. 61, July 2026
I authored and published the scientific article “Building Intelligent Data Quality Agents Using Large Language Models,” examining the application of large language models to intelligent data-quality engineering. The publication focuses on using AI to move beyond traditional reactive data cleaning toward systems capable of identifying data-quality problems, interpreting anomalies, and supporting more automated approaches to data validation and remediation.
The article reflects the same engineering direction underlying my broader work in public health and healthcare data: building trustworthy data foundations before applying advanced analytics and artificial intelligence. The publication explores how intelligent data-quality agents can help organizations identify issues such as inconsistent, incomplete, or anomalous data and make data-quality processes more scalable and accessible.
The article was published in the International Integralize Scientific Journal, Technology Section, Volume 6, No. 61, July 2026, and carries ISSN 3085-654X. The publication certificate issued by the journal identifies me as the author and records the digital publication of the article.
Why this recognition matters:
-
Demonstrates contribution to the emerging intersection of data engineering, artificial intelligence, and automated data quality.
-
Extends my professional work beyond implementation into independent technical research and knowledge creation.
-
Connects directly with my development of AI-assisted data-quality approaches for complex organizational and public-sector data environments.
-
Demonstrates that my work encompasses not only building production data systems but also developing and communicating new approaches to intelligent data engineering.
Publication:
Read the Scientific Article
Publication Certificate:
View Publication Certificate
DOI: 10.63391/7zdnxw28
___________________________________________________________________________________
MEDIA & PRESS COVERAGE
National and International Media Coverage of My Data Engineering Work
My work in public health data modernization, cloud architecture, artificial intelligence, data quality, and nonprofit technology has also received coverage across multiple technology, business, and news publications.
The coverage is notable because the articles discuss my work in the context of a broader problem: how public-sector organizations, healthcare environments, and nonprofits can use modern data engineering and AI while operating with fragmented systems, limited technical resources, and strong requirements for trustworthy information.
1. Big News Network: AI, Trustworthy Data & Public Health
“The Next Frontier of Artificial Intelligence Isn't Chatbots, It's Trustworthy Data”
Big News Network published a feature examining my engineering philosophy around the relationship between AI, data quality, cloud architecture, and public health.
The article specifically discusses my work at Bear River Health Department and describes the integrated use of Apache NiFi, Google BigQuery, Python, and Metabase to replace fragmented spreadsheet-based reporting. It also reports that the resulting platform supported approximately 50 dashboards serving programs across three Utah counties and highlights the platform's operating cost of under $220 per month.
The article further discusses my work on an AI-powered Data Quality Agent, describing the use of generative AI to profile datasets, identify anomalies, and suggest potential remediation while providing explanations for data-quality problems.
Why this matters:
The coverage presents my work not simply as dashboard development, but as an engineering approach centered on trustworthy data infrastructure, affordability, automation, and responsible AI for public-sector and healthcare environments.
Read the Big News Network feature
2. The Hans India: Public Health Data Infrastructure
“How Venkata Sravan Namuduri Is Building Data Infrastructure for the Future of Public Health”
The Hans India feature examines my work as a Data Engineer supporting public health modernization through the CDC Foundation's Workforce Acceleration Initiative.
The article describes the data challenge at Bear River Health Department, including fragmented information across programs and systems, and discusses my work developing an integrated environment for converting raw information into usable public health intelligence. It specifically highlights automated data pipelines, BigQuery, Apache NiFi, KPI dashboards, data modeling, data-quality processes, and the use of open-source/cloud technologies.
A particularly important aspect of the coverage is its discussion of affordability and replication: the article reports that the dashboard platform operates at under $220 per month and describes the approach as potentially useful for other health departments operating under similar resource constraints.
The article also connects my work to nonprofit technology and healthcare research, including my work with Big Brothers Big Sisters and the University of North Texas.
Why this matters:
The feature frames my work as a systems-level public health modernization effort, rather than simply an individual analytics implementation.
3. People Brasil: Data Engineering & Social Impact
“Da Engenharia de Dados ao Impacto Social: Como Venkata Sravan Namuduri Transforma Tecnologia em Soluções para a Saúde Pública”
People Brasil featured my professional work in the context of the connection between data engineering and social impact, highlighting my work in public health technology and modernization.
The feature is particularly relevant to the broader story of my career because it connects technical engineering work with its application to public health and community-serving organizations, rather than presenting data engineering purely as a commercial technology function.
Why this matters:
The coverage helps demonstrate the broader social-impact dimension of my technical work: applying data engineering and technology to organizations whose mission is to serve communities.
Read the People Brasil feature
4. Handelsblatt: Data Technology, Public Health & Nonprofits
“Tecnologia de dados ganha protagonismo na saúde pública e amplia eficiência de organizações sem fins lucrativos”
Handelsblatt published a feature discussing the increasing role of data technology in public health and nonprofit organizations and included my work as an example of this broader shift.
The article describes my focus on building platforms that automate processes, integrate different data sources, and support decision-making for public organizations and third-sector organizations. It specifically discusses my work through the CDC Foundation's Workforce Acceleration Initiative, including automated pipelines, real-time analytical dashboards, and intelligent data processing.
The article also discusses my philosophy of developing technology that can remain sustainable for organizations with small technical teams and constrained budgets, emphasizing systems designed for autonomy, sustainability, and reduced operational cost.
It additionally references my nonprofit work with Big Brothers Big Sisters and Mesa Farm and my University of North Texas research involving cancer survival.
Why this matters:
This coverage reinforces the central theme of my work: using data architecture and automation to make advanced technology practical for public-sector and nonprofit organizations.
5. IstoÉ Negócios: Technology, Public Health & Organizational Efficiency
“Tecnologia de dados ganha protagonismo na saúde pública e amplia eficiência de organizações sem fins lucrativos”
IstoÉ Negócios published the same broader feature examining how data technology, automation, cloud computing, and AI are increasingly being applied to public health and nonprofit environments.
The article identifies my work as an example of data engineering applied to these challenges and discusses my focus on sustainable technology environments, automated data pipelines, integrated public health platforms, and decision-support systems. It also references my WAI work, NACCHO 360 participation, nonprofit projects, and University of North Texas healthcare research.
Why this matters:
The coverage places my technical work within a broader discussion of how technology can improve the efficiency and sustainability of organizations serving the public.
___________________________________________________________________________________
PEER REVIEW & SCIENTIFIC SERVICE
Ad Hoc Reviewer: International Integralize Scientific Journal
In July 2026, I was formally recognized by the International Integralize Scientific Journal for serving as an ad hoc reviewer of scientific manuscripts in the field of Technology.
The journal's certificate identifies me as a researcher who contributed to the scientific community through the rigorous and impartial evaluation of submitted scientific articles. The certificate records that I reviewed five manuscripts covering topics including behavioral assessment instruments in technology contexts, health technology and telemedicine, cybersecurity, Brazil's information-technology market, and Agile methodology adaptations for IoT projects.
The certificate was issued in Florianópolis, Santa Catarina, Brazil, on July 17, 2026, and is signed by the journal's editorial representatives, including the Editor-in-Chief.
What This Recognition Demonstrates
-
Peer-level scientific trust: I was selected to evaluate research submitted by other researchers rather than solely producing work of my own.
-
Technical judgment: The role required assessing scientific manuscripts for quality, methodology, relevance, and technical contribution.
-
Breadth of expertise: The reviewed manuscripts covered technology, health technology, cybersecurity, information systems, and IoT methodologies.
-
Contribution to the research community: The review activity contributed to the journal's process of evaluating and disseminating scientific work.
-
Independent recognition: This role was formally documented by the journal through an official ad hoc reviewer certificate.
Reviewer Certificate:
View Ad Hoc Reviewer Certificate
___________________________________________________________________________________
CDC Foundation LinkedIn Feature — Workforce Acceleration Initiative (WAI)
I was honored to be featured by the CDC Foundation through its official Workforce Acceleration Initiative (WAI) spotlight, recognizing my contributions as a Data Engineer supporting the Bear River Health Department in Utah.
The feature highlighted my work modernizing public health reporting systems and helping develop scalable dashboard solutions that improve how public health teams access, analyze, and use critical data for operational and community decision-making.
As part of the national WAI initiative, the CDC Foundation placed data and technology professionals across public health agencies throughout the United States to strengthen public health infrastructure, analytics capabilities, and modernization efforts.
In the feature, I shared insights about my experience supporting Bear River Health Department’s data modernization initiatives and my interest in continuing to contribute to the future of public health technology and analytics.
Feature Overview
-
Featured through the CDC Foundation’s official LinkedIn/public outreach platform
-
Recognized as part of a national workforce modernization initiative
-
Highlighted for contributions to public health analytics and dashboard modernization
-
Represented the Bear River Health Department’s data modernization efforts through WAI
Scalable Public Health Innovation
A key part of this initiative involved helping design and implement a scalable, low-cost public health data architecture capable of supporting multiple health departments and public sector programs.
The platform was built using modern cloud technologies, automated ETL pipelines, and open-source analytics solutions to reduce dependency on expensive enterprise systems while improving reporting efficiency, accessibility, and real-time visibility for leadership teams.
This scalable architecture model was designed with replication in mind — enabling rural and budget-constrained health departments to adopt modern analytics infrastructure at significantly reduced operational costs while maintaining flexibility, performance, and long-term sustainability.
Click below to watch the featured spotlight video posts and learn more about the initiative.
___________________________________________________________________________________
Official Presenter — National Public Health Data Innovation Showcase
CDC Foundation | WAI Annual Summit 2026
Selected as an official presenter at the CDC Foundation’s Workforce Acceleration Initiative (WAI) Annual Summit 2026, a national-level public health conference, to present my work on modernizing public health data systems using scalable, low-cost architecture.
This presentation was formally reviewed and approved for national showcase, recognizing the project’s innovation, practical impact, and potential for replication across health departments.
Presentation Focus:
The presentation highlighted a transformational shift in public health analytics, demonstrating:
-
Transition from manual, fragmented reporting systems to an integrated, real-time data platform
-
Application of cost-efficient, cloud-native and open-source technologies in government/public health environments
-
A scalable and replicable model designed specifically for resource-constrained and rural health departments
-
Measurable improvements in operational visibility, reporting speed, and decision-making efficiency
National Visibility & Recognition:
-
Selected among WAI initiatives to be presented at a national conference attended by public health leaders, program managers, and data professionals
-
Positioned as a representative implementation of modern data engineering practices in public health
-
Contributed to knowledge-sharing discussions around data modernization, system scalability, and digital transformation
Forward-Looking Contribution:
As part of the presentation, I also introduced early concepts around AI-enabled data interaction, including:
-
Exploration of Agentic AI-based data agents for non-technical users
-
Enabling natural language-driven access to public health data systems
-
Advancing the vision of self-service, intelligent analytics in public sector environments
Closing Statement:
This selection as an official presenter reflects recognition of my work at a national level, demonstrating not only technical execution but also the ability to contribute to broader public health data innovation conversations and influence scalable solutions beyond a single organization.
Click below to learn more about the initiative.
___________________________________________________________________________________
From Paper to Pixels: Rebooting Public Health Data – CDC Foundation Feature
I was honored to be featured by the CDC Foundation for my work transforming the Bear River Health Department’s data systems from manual, paper-based workflows into an automated, unified, modern data platform.
As a Data Engineer hired under the CDC Foundation’s Workforce Acceleration Initiative (WAI), I partnered closely with Bear River leadership to rebuild the department’s analytics foundation—end-to-end.
My Role & Impact:
-
Designed and maintained the data warehouse architecture powering the entire KPI dashboard ecosystem.
-
Built automated, low-cost, cloud data pipelines (BigQuery, NiFi, open-source BI) that replaced outdated manual processes.
-
Led user-centered dashboard design: meeting with clinical, environmental health, behavioral health, and community staff to simplify visuals, improve readability, and ensure every chart answered a real question.
-
Ensured KPIs were accessible, fast, and accurate—helping leadership quickly understand program effectiveness and spot areas needing support.
-
Optimized accessibility with clear labels, large fonts, numeric callouts, and simplified tooltips for non-technical staff.
-
Helped reduce an enterprise-level dashboard cost (normally tens of thousands of dollars) to under $220/month, making the solution replicable by rural and budget-constrained departments nationwide.
-
Delivered a system that empowers Bear River to educate lawmakers, strengthen outreach, and improve community health outcomes with real-time data.
Quoted in the Story
“Changing from manual inputs to automation is like switching from gasoline cars to electric cars. This dashboard lets the health department make decisions more quickly and stop wasting time searching for data.”
— Venkata Namuduri, CDC Foundation Data Engineer
This feature represents one of the most meaningful projects of my career—solving real public health challenges through low-cost, scalable technology, and contributing to a national data modernization movement.
Read the Full Story
Click below to read the complete CDC Foundation feature:
From Paper to Pixels: Rebooting Public Health Data
.jpg)
.jpg)
___________________________________________________________________________________
Workforce Acceleration Initiative (WAI) Impact Newsletter – CDC Foundation
I was honored to be highlighted in the CDC Foundation’s national WAI Impact Newsletter, which features impactful stories, quotes, and accomplishments from public health data modernization professionals across the United States. The newsletter recognized my ongoing contribution to transforming data systems at the Bear River Health Department, strengthening data practices, and advancing modern public health infrastructure.
This national recognition places my work alongside other WAI leaders and data experts who are modernizing public health data systems across the country — from interoperability and standards, to EHR integration, to advanced analytics and real-time disease detection.
My Role & Contribution (as featured in the newsletter)
-
Supporting public health modernization as a Data Engineer for Bear River Health Department, under the CDC Foundation’s Workforce Acceleration Initiative (WAI).
-
Enhancing data system interoperability, promoting modern data exchange formats, and strengthening health department analytics foundations.
-
Helping implement efficient, scalable, and transparent data systems that improve how public health teams use, share, and trust critical health data.
-
Driving best practices in data quality, governance, and automation within the department.
-
Representing the workforce cohort nationally through insights, shared learning, and participation in WAI community initiatives.
Quoted in the Newsletter
“I am hopeful that my involvement with WAI ensures that the CDC Foundation's contributions are recognized and leveraged effectively in academic and practical settings.”
— Venkata Sravan Namuduri, Data Engineer, Bear River Health Department
This quote highlights my commitment to ensuring that the data systems, dashboards, and modernization strategies we build continue delivering value long after project completion — supporting both ongoing public health work and future innovation.
View the WAI Newsletter Feature
Click here to read the full WAI Impact Newsletter
WAI News Letter
In addition, my work was featured as a testimonial on the CDC Foundation’s official Careers website, recognizing my contributions to public health data modernization through the Workforce Acceleration Initiative (WAI).
Note: The CDC Foundation periodically updates its website content. If my testimonial, photograph, or WAI employee feature is no longer visible on the current Careers page, please refer to the archived PDF below, which preserves the version of the CDC Foundation Careers page where my testimonial was featured.
Click here to view Testimonial
