Sai Swetha Bodlapati

About

Data engineer at Fidelity who built and operated a real-time financial transactions lakehouse on AWS/Databricks, processing millions of records daily with Kafka streaming. Demonstrated strong reliability and data quality practices (watermarking, idempotent Delta writes, validation/reconciliation, observability) and delivered measurable improvements (~30% faster jobs and ~30% fewer data issues) while enabling trusted gold-layer analytics for downstream teams.

Key Strengths

• Owned end-to-end lakehouse pipeline for financial transactions (ingestion to serving) processing millions of transactions daily
• High-throughput streaming design (Kafka thousands of events/sec) with late-arriving/out-of-order handling via watermarking and checkpointing
• Strong data quality engineering: schema validation/enforcement, deduplication, reconciliation, and alerting to prevent bad downstream data
• Reliability/operability focus: retries, checkpointing, DLQ patterns, and end-to-end observability (CloudWatch/Prometheus/Grafana)
• Performance optimization at scale (partitioning, caching, broadcast joins) improving Spark job runtime ~30%
• Safe versioning/backward compatibility using Delta Lake schema evolution/time travel and deprecating fields vs breaking changes
• Built curated gold-layer datasets and reusable SQL views that increased downstream adoption and sped up dashboard/insight delivery

Experience

Education

Awards

• Key Contributor Award for Optimized Spark and Databricks ETL pipelines
• Outstanding Team Player Award for implementing data validation, monitoring, and alerting frameworks
• Excellence in Innovation Award for strengthening data governance and security (HIPAA-compliant analytics and regulatory audits)

Skills

Languages

Certifications