Millions of Americans count on public assistance for healthcare, food, and emergency cash. Are the agencies running these programs? They’re dealing with old systems that don’t talk to each other, data trapped in silos, and delays that can stretch for weeks. More than 71 million Americans were on Medicaid rolls by early 2025, and eligibility systems handle billions of transactions across dozens of federal and state programs.
Volume is only part of the problem. Benefits administration today means getting real-time data to flow between legacy platforms, new cloud setups, and reporting tools used by everyone from caseworkers to federal auditors. When pipelines break or slow down, people feel it immediately. An eligibility check that takes too long means a family loses coverage. A bad data report can freeze funding that thousands depend on.
Data engineering in this world isn’t some abstract technical exercise. It’s infrastructure. And the people building it need more than coding skills; they need to understand policy rules, compliance red tape, and how government agencies actually operate when downtime isn’t an option.
Building systems that survive policy chaos
Srinubabu Kilaru has spent years in this space. As a Senior Data Lead and Business Analyst working on government health and human services projects, he’s dealt with the unglamorous problems that determine whether these systems actually work. His job has been creating data architectures that pull information from old welfare platforms, run it through modern cloud tools, and deliver reports that stakeholders need urgently.
One major piece of work: reusable data frameworks on Azure Databricks that could handle Medicaid, SNAP, and TANF without building custom pipelines every time someone needed a new report. He integrated ADF, PySpark, and DBT to create modular transformation layers. State agencies could adjust when regulations changed or new data sources showed up. This wasn’t theoretical. It was built to survive real-world government chaos.
“The hardest part is not the technology,” Srinubabu Kilaru says. “It’s understanding how policy translates into data requirements, and then building systems flexible enough to handle the next policy change without starting from scratch.”
He also added AI-driven anomaly detection straight into ETL workflows. Python automation flagged weird patterns in benefit applications and eligibility data before they hit reporting systems. That cut down on manual reviews for state teams already stretched thin. The approach used lightweight machine learning models that ran inside existing pipelines, no separate infrastructure, no specialised staff needed to babysit them.
Making governance work across sprawling projects
Pipeline work was just one piece. He also implemented the Unity Catalogue for data governance across multiple projects. The problem it solved: different teams building their own solutions with no common standards for metadata, access controls, or tracking where data came from. Centralized governance meant less duplicate work and easier audits without grinding operations to a halt.
He built CI/CD workflows to end-to-end automation that let data teams push changes faster without breaking federal security rules. This meant coordinating with DevOps people, security officers, and agency stakeholders to automate testing and deployment that used to take weeks of manual checks. Shorter release cycles. Fewer errors from human mistakes.
Then there’s the business intelligence side. He designed Power BI semantic models and templates for state agencies to report on program performance, caseloads, and compliance. Not standard dashboards, these were built around the KPIs that federal and state leaders actually cared about, based on working directly with CURAM experts and policy teams.
“You cannot just hand someone a visualization and expect it to work,” he says. “You need to understand what decisions they are trying to make and what questions they get asked when things go wrong.”
Experience across industries with high stakes
What distinguishes his approach is how much ground he’s covered. Government, healthcare, finance, and manufacturing, he’s worked in all of them. Real-time streaming on Kafka. Batch processing in Snowflake and Informatica. That kind of cross-industry background teaches you which patterns actually perform under pressure and which ones break down when requirements shift or data volumes spike.
Healthcare projects meant dealing with patient data flows under HIPAA rules, while keeping performance responsive no room for lag. Financial services meant building pipelines that reconcile transactions across multiple systems with zero tolerance for error. All of that feeds into government work, where regulators are always watching and system failures have public consequences.
he also spent time on mentorship and documentation, training junior developers, and writing solution design templates other teams can reuse. Undervalued work that often gets overlooked in technical roles, but is critical when you’re on a big project with high turnover and knowledge leaving every few months.
The push to modernize public sector tech
His work fits into a bigger shift happening across government agencies, trying to modernize their tech stacks. The Centres for Medicare & Medicaid Services has been pushing states to adopt modular, cloud-based systems that integrate with federal data hubs and handle real-time eligibility checks. Cost pressures are driving it, but so is the need for better program integrity. Old systems make it too easy for fraud to slip through and too hard to respond when policies change.
Modernization isn’t just about migrating to the cloud. It means rethinking data flows, how you enforce governance, and how technical teams work with policy people who speak a different language. Engineers like him bridge that gap, turning regulatory requirements into technical specs and making sure today’s systems can handle whatever changes come next.
The numbers tell the story. A 2024 Government Accountability Office report found Medicare and Medicaid recorded over $100 billion in improper payments during fiscal 2023, mostly from eligibility mistakes and missing documentation. Better data systems won’t solve everything, but they’d give agencies tools to catch problems sooner and respond faster when things break.
What’s ahead
Public sector data systems are going to keep evolving, and the need for people who can handle both technical complexity and policy constraints is only growing. Next-generation government IT projects will demand tighter connections between cloud platforms, AI automation, and regulatory compliance frameworks. They’ll need people who get that good engineering here isn’t about adopting the latest tools, it’s about building reliable systems that serve the public and don’t fail when it matters most.
Srinubabu Kilaru’s work demonstrates what that looks like. Engineering anchored in real constraints. Built to outlast the current project cycle. Focused on outcomes that matter to people who’ll never see the code but need it to work right every day.
