Ashwini Pankaj Mahajan

Across more than eight years in AI/ML-enabled data engineering and enterprise systems, Ashwini Pankaj Mahajan has built a career around a practical, high-stakes problem: how to turn fragmented healthcare data into trusted, compliant, and decision-ready intelligence. Working in environments where reporting accuracy affects regulatory readiness and where analytics informs patient-care operations, Mahajan has repeatedly taken on modernization efforts that combine cloud migration, systems integration, and automated data-quality controls.

Her work is anchored in healthcare payor ecosystems that serve large member populations, where legacy pipelines, inconsistent source systems, and audit demands often collide. In these settings, Mahajan has operated as both a program leader and hands-on systems engineer—driving delivery discipline while also shaping the technical core: ETL design, validation logic, and repeatable control frameworks that reduce defects and stabilize downstream analytics.

At Sentara HealthPlans, Mahajan led the CMS IO data migration and unified reporting program, consolidating datasets across Optima Health and Virginia Premier into a single reporting structure aligned to compliance expectations. This was not a simple data move: it required reconciliation of incompatible data conventions, new integration logic, and the introduction of automated quality gates. Mahajan designed and managed SSIS-based ETL pipelines and refined T-SQL mapping and validation, reducing manual effort by roughly 40% while improving reliability through structured regression cycles and systematic data checks. In parallel, she served as a second-level code reviewer—using engineering governance to raise delivery quality across multiple teams and to ensure that compliance requirements were implemented as enforceable controls, not informal checks.

Beyond reporting, Mahajan’s role extended into AI enablement—working with data science teams to improve member-level predictive modeling by making operational and clinical data usable for machine learning. Her contributions focused on the “last mile” between raw source data and model-ready features: translating health-plan data into structured, consistent inputs, identifying gaps that distorted model behavior, and aligning the feature pipeline with operational, clinical, and compliance constraints. The result was not simply better data; it was a tighter coupling between predictive modeling and real-world healthcare operations.

At Molina Healthcare, Mahajan led the migration of legacy reporting workloads into Azure Databricks, replacing multi-hour SQL processes with distributed processing suitable for high-volume healthcare data. This effort required re-architecting logic for parallel execution rather than replicating existing scripts. Mahajan also developed Power BI dashboards to provide leadership with near-real-time visibility into population health trends, utilization, and cost opportunities, and implemented automated anomaly detection to strengthen Cardinal Care audit readiness.

Across roles, a consistent theme in Mahajan’s work is automation as governance: building repeatable ETL, audit checks, regression frameworks, and monitoring dashboards that convert manual data stewardship into systematic process. Her toolset includes SSIS, SQL, Python/pandas-based validation, and operational dashboards that make data quality observable for both technical and business stakeholders.

Earlier, at Vaneera HiTech, Mahajan designed and built an Enterprise Resource Management (ERM) platform focused on billing optimization and operational transparency. By automating previously manual, error-prone billing and reporting workflows, she delivered tangible business outcomes: saving 40+ analyst hours per month, reducing billing errors by ~60%, and contributing to a ~17% revenue increase through improved utilization and forecasting visibility—demonstrating the same throughline visible in her healthcare work: improving outcomes by engineering reliable, automated systems.

Taken together, Mahajan’s career reflects a practitioner who builds data ecosystems that organizations can trust—modernizing platforms, integrating disparate systems, and embedding validation into pipelines so that analytics, ML, and compliance reporting operate on consistent ground truth.