Headline

Data & AI Engineering at Visa

About

Senior Engineer specializing in building data platforms to power agentic AI at scale, following cloud-agnostic and platform-agnostic principles. I have worked extensively with large-scale datasets in the US healthcare domain, including payer, provider, claims and pharmacy data, designing systems that can reliably process, validate, and model billions of records. I have delivered high-impact projects for some of the largest US payers, working closely with both technical and business stakeholders. While I bring strong client-facing and delivery experience, my primary passion is building robust platforms, clean data models, and reliable pipelines that scale. I have built end-to-end lakehouse architectures that centralize vendor and client data, enforce schemas, enable automated QC frameworks, and power analytics-ready marts. I focus on turning complex, messy healthcare data into clean, production-grade data products that downstream teams can trust. Currently focused on: • Distributed data processing (Databricks, Spark, Delta Lake) • Cost-optimized agentic AI on realtime data • Cloud-native data engineering (Azure, AWS) • Performance tuning and cost management • Data modeling • Automation and data quality Always open to conversations around data engineering, platform design, and building high-scale data products.

Location

Bengaluru

AI stack

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Work experience

Senior Engineer (Data & AI) at Visa, Mar 2026 - Present · 8 mos, Bengaluru, Part of the Personalization Insights team, building data foundations that power agentic AI on petabyte-scale data to better understand and predict consumer behavior in real-time.

Forward Deployed Engineer / Consultant at ZS, Jun 2021 - Mar 2026 · 4 yrs 10 mos, Bengaluru, • Delivered $20M+ in yearly savings by architecting data pipelines processing 400+ GB per run to power finance-grade analytics strategies at scale. • Built and owned business-critical PySpark pipelines aggregating large-scale claims data to compute actual savings, vendor commissions, and finance reconciliations, with end-to-end auditability and data lineage. • Drove a 95% reduction in pipeline runtimes by replacing legacy workloads with distributed PySpark, optimizing joins and partitioning, pruning I/O, and leveraging Delta Lake optimizations including Z-ordering and caching. • Migrated legacy on-prem Hadoop pipelines to PySpark on Databricks, standardizing ingestion for 10+ external vendor feeds using schema normalization, metadata-driven configurations, and automated data quality validations. • Acted as Subject Matter Expert for Payment Integrity, translating complex domain logic into deterministic transformations, controls, and scalable data models. • Led stakeholder interviews and requirements discovery, converting ambiguous business problems into a prioritized backlog, well-defined user stories, and measurable acceptance criteria. • Owned UAT and production readiness, leading in-person test execution and sign-off with client stakeholders in Chicago. • Earned executive-level recognition for technical excellence and leadership.

Education

Bachelor of Technology - BTech, Information Technology, SVKM's Narsee Monjee Institute of Management Studies (NMIMS)