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I was initially focused on optimizing our analytics platform—a cutting-edge system capable of deep insights and financial projections for Value-Based Care. Yet no matter how sophisticated our algorithms became, we hit a massive bottleneck: the data. Incomplete or delayed data meant we couldn’t unlock meaningful results, and the pipeline itself was a tangle of messy CSVs, secure emails, and manual approvals.
At that time, eight full-time data engineers were juggling eight monthly contracts, each wrestling with last-minute requests. Expenses were mounting, our company was exploring offshore engineering in India, and leadership wanted more. Then, one day, my supervisor sat me down and: “We need to scale from 8 contracts a month to 150+ and all within a five-day SLA. You’ll form and lead a distributed team—some in India, some in the US—to solve this. Propose a solution and make it happen.”
It felt impossible—over a 10x jump in capacity, tighter budgets, a new offshore model, and a ticking clock on data timeliness. But I knew that if we pulled it off, we’d transform how our entire organization handled data. Our analytics engine would finally get the robust, near real-time feeds it deserved, and we’d pave the way for true data-driven decisions in population health and Value-Based Care.
As our organization expanded into value-based care, we had to unify data from multiple payers—each delivering CSV, Excel, or HL7 feeds—so we could provide robust population health analytics. While we initially handled just a handful of monthly data feeds, leadership aimed to scale to over 150+ without blowing up engineering costs. Meeting an aggressive 7-day SLA demanded a scalable, automated platform that would unify these disparate formats and reduce our heavy reliance on manual processes.
Our legacy pipeline relied on one-off scripts, ad-hoc file handling, and no single source of truth for contract metadata. Each new contract integration took weeks of developer time to wrestle with messy data formats—slowing analytics and threatening SLAs. We needed an end-to-end solution capable of ingesting and normalizing diverse payer data at scale, ensuring we could power value-based care insights without drowning in constant rework.
As Director of Product and Data Engineering, I orchestrated the overall vision, roadmap, and cross-functional execution. This involved bridging executive stakeholders—like the VP of Population Health and COO of Pop Health—with product and engineering teams in both the US and India. Tapping into my strengths in strategic thinking and relationship-building, I ensured everyone—from data ops to regional executives—stayed laser-focused on our 5-day SLA for data availability.
To keep teams aligned across time zones, I coordinated daily stand-ups and weekly demos, fostering a cohesive, agile culture. Early on, I collaborated closely with principal engineers to architect our initial solution; as we scaled, I mentored BAs and project managers to adopt a product mindset, forming smaller, focused sub-product teams. I introduced continuous discovery and delivery practices, lean product thinking, and story mapping. This blend of technical architecture and customer-centric iteration ensured each step delivered real user value while meeting executive priorities.
Before execution began, I led a series of strategy sessions with my supervisor and colleagues to validate our internal product-market fit. Here is where we inserted the Business Model Canvas (BMC) and Value Proposition Design (VPD) into our workflow:
Business Model Canvas (BMC): We mapped our cost structures, potential revenue impact, and key payer partnerships, ensuring our data platform aligned with broader business goals in value-based care. This clarified our “where to play” and “how to win,” recognizing the need for automated ingestion to serve 150+ VBC contracts without exploding costs.
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Value Proposition Design (VPD): We pinpointed user segments (data ops, regional executives, analytics teams), identifying their jobs, pains, and gains. This allowed us to shape a scalable, cloud-based engine that tackled messy data ingestion—transforming CSV/Excel complexities into a frictionless experience for stakeholders.
Armed with these strategic insights, we set a high-level vision for an automated, end-to-end data ingestion pipeline. Everyone from data engineers to executive leadership embraced the platform’s why: it wasn’t just about cleaning data—it was about enabling real-time decisions in population health and value-based care.
For data ops teams and healthcare leaders struggling to unify messy CSV/Excel feeds from multiple payers,Hungry Wizard is a scalable data ingestion platform that automates, validates, and consolidates files, meeting strict SLAs and empowering real-time analytics for 150+ value-based care contracts.Unlike ad-hoc manual workflows or fragmented ETL scripts,we provide an end-to-end pipeline that slashes overhead, accelerates insights, and seamlessly scales with your VBC growth.
With clear strategic goals defined, we adopted an agile, lean product mindset to deliver iteratively. We used user-story mapping sessions to spotlight high-impact features, high-risk features, gaps in thinking and establishing clear workflows.
Our lean approach ensured we quickly validated each sub-product's value. Weekly demos and daily stand-ups bridged the India-US time zones, maintaining alignment with the strategic vision established in our BMC/Value Prop work. This cycle of feedback and iteration helped us refine each sub-product continuously, hitting our 5-day SLA goals without losing sight of the bigger picture: enabling scalable, parallel data ingestion for 150+ VBC contracts.
Risk Arrangement Data (RAD) is a centralized repository for all value-based care (VBC) contract metadata. It consolidates key contract details (e.g., payer information and contract terms) that were previously scattered across ad hoc spreadsheets. By serving as a single source of truth, RAD ensures downstream processes always have consistent, up-to-date contract parameters in real time.
RAD stores a structured set of metadata fields for each contract. Key fields include:
Under the hood, RAD runs on a relational database with a simple user interface for data entry. Other pipeline components query RAD on-the-fly to retrieve contract configurations, ensuring uniform rules and definitions across all processes.
Managing payer file transformations used to be cumbersome, requiring spreadsheets of rules and custom-coded logic for each new contract. This approach led to delays (onboarding could stretch to a month) and frequent errors (misinterpretations between business analysts and developers).The Contract Toolkit solves this by providing a low-code, UI-driven transformation engine that replaces manual documentation and developer hardcoding. It empowers analysts to define and refine data transformation logic themselves—no code required. This self-service model cuts turnaround time dramatically and improves the accuracy of ingested data.
Business analysts and data managers—those who truly understand incoming payer file formats—are the Toolkit’s key users. Previously, they documented every detail in Excel, waited for developers to implement the logic, and endured repeated clarifications. By transferring that responsibility to a web-based platform:
A simple, step-by-step UI guides BAs through defining file logic, drawing on relevant contract data from RAD. They select the incoming file, map source columns to standardized fields, and configure transformations or filtering rules—no coding required. All such rules compile into PySpark jobs for large-scale processing:
Shifting transformation logic to the BAs who know the data best has drastically improved efficiency and quality:
Together, these components handle file ingestion from payer sFTP servers and validate each dataset before it enters the core engine. Although simpler than other products, they form the first crucial steps of our monthly pipeline.
We automate sFTP polling every hour, referencing RAD to know which files to expect from each payer. This removes hours of manual checking, placing files in correct folders for the pipeline. It's a straightforward step but essential for near real-time ingestion built using Azure Function Apps.
Right after acquisition, the precheck tool validates each file against expected columns/formats. If mismatches appear, we flag them immediately, preventing downstream errors. This simple but critical step cuts ingestion errors drastically.
Our Core Processing Engine is the heart of our data pipeline, replacing sequential SQL stored procedures with a parallelized PySpark architecture. This shift enabled us to tackle massive volumes of payer data in near-real time, aligning with modern big-data practices seen at leading tech firms.
Transitioning from stored procedures to a distributed Spark engine demanded a steep learning curve. We faced memory constraints and data skew issues early on, prompting us to:
By adopting PySpark and a truly parallel design, we revolutionized our data processing. Teams gain near-instant visibility into new contract data, supporting real-time analytics and broader value-based care initiatives. The Core Processing Engine turned an outdated, slow ingestion workflow into a streamlined, modern data pipeline—empowering us to scale far beyond our previous limits without sacrificing accuracy or control.
Purpose: Ensures data accuracy by comparing final engine outputs to raw files and previous month baselines. Catches anomalies (e.g., missing columns, unexpected record count) before data is marked “valid.”
Prevents corrupted or incomplete data from being warehoused—reducing reprocessing overhead and building trust in final reports.
Purpose: Move the validated data to a Cloud Data Warehouse, making it accessible for analytics teams and BI tools.
How It Works: After passing all validation checks, data is inserted or updated in the CDW, typically via a structured SQL or managed ingestion process.
Impact:Business stakeholders can confidently query near real-time data knowing it’s passed rigorous checks. Eliminates manual steps that once plagued monthly ingestion.
Challenge: DataOps relied on emails and manual checks to track pipeline progress. Issues were often discovered only after SLA breaches or significant delays. This reactive approach made it hard to meet strict 5-day SLAs consistently.
Solution: A centralized dashboard to provide live visibility into each pipeline stage. It transformed monitoring from reactive to proactive by introducing instant alerts on delays or errors. The dashboard also includes integrated controls to trigger pipeline re-runs or tasks directly from the UI, enabling immediate intervention without switching tools.
API-Driven Integration: A custom API layer connects the dashboard with the data pipelines. It logs each pipeline stage’s start/end times and outcomes to a central store, updating SLA status in real time. The same API accepts commands from the UI to initiate or re-run pipeline tasks on demand, eliminating the need to manually run scripts or use separate tools.
Publish-Subscribe Architecture: The system leverages a pub/sub model (WebSockets) to push live status updates to all users. Multiple DataOps engineers can have the dashboard open and see changes simultaneously (for example, a stage completion or an alert) without any page refresh. This real-time broadcast ensures the whole team shares up-to-the-second information and can act in concert.
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