SD

Résumé

Data & AI Engineer

I build the pipelines, data stores, and services behind analytics and AI products.

Credentials

Experience

  1. Senior Data Engineer at Quantiphi Inc.

    Data infrastructure for healthcare retrieval and AI workflows.

    • Building the data foundation for a healthcare RAG platform. Bringing clinical, operational, and knowledge content into Cloud Storage and BigQuery, with lineage, access controls, and vector indexes for retrieval.
    • Developing multi-step agents with LangGraph, FastAPI, Vertex AI, and Cloud Run. Adding tool calling, memory, audit logs, and human approvals so teams can review what the agents do.
    • Python
    • Cloud Storage
    • BigQuery
    • Dataflow
    • Vertex AI
    • LangGraph
    • FastAPI
    • Cloud Run
  2. Data Engineer at Wells Fargo · via Capgemini America

    Cloud data engineering for regulated email records.

    • 65% improvement in data freshness
    • 43 email governance guardrails
    • 20 teams with on-demand access
    • Moved on-premises batch email governance to Google Cloud, giving 20 downstream teams on-demand access.
    • Connected Pub/Sub and Dataflow with Airflow and Cloud Functions for near-real-time email ingestion. Tuned autoscaling, batching, and message acknowledgments, improving data freshness by 65%.
    • Added 43 guardrails for PII, legal, finance, and cybersecurity requirements using redaction, exclusion, and audit tagging.
    • Helped build a layered lakehouse with BigQuery, Spark, and Apache Iceberg, serving 14 teams across three domains and refreshing 20+ views hourly.
    • Stabilized Spark loads across 10 workstreams by fixing skew, memory pressure, shuffle spills, and schema drift.
    • Pub/Sub
    • Dataflow
    • Airflow
    • Cloud Functions
    • BigQuery
    • Spark
    • Apache Iceberg
  3. Data Specialist at University of Maryland

    Data pipelines and retrieval tools for academic operations.

    • 20M+/day live learning-platform events
    • 12+ departments using a shared data source
    • 100K+ survey responses in a retrieval prototype
    • Streamed 20M+ live learning-platform events daily into BigQuery, creating a shared data source for 12+ departments.
    • Replaced weekly snapshots with hourly incremental loads using Parquet, Amazon S3, and Redshift, reducing data latency from days to hours.
    • Built a retrieval-augmented generation (RAG) prototype over 100K+ survey responses. Used semantic chunking, retrieval, and knowledge graphs to surface themes and quotes, reducing manual review.
    • Google Cloud
    • BigQuery
    • Parquet
    • Amazon S3
    • Redshift
  4. Senior Software Engineer — Data Engineering at Tiger Analytics

    Self-serve data infrastructure for enterprise clients.

    • 6 enterprise clients
    • 30+ data quality rules
    • 4 days → hours data onboarding turnaround
    • Worked with a senior architect from design through rollout on a cloud data platform adopted by six enterprise clients over two years.
    • Built containerized FastAPI services behind a self-service interface, using Elastic Load Balancing and API Gateway to connect ingestion, source configuration, and asset monitoring.
    • Built a data quality engine with Scala, Spark, and Deequ. Added 30+ business rules and dashboards that made anomalies easier to find and investigate.
    • Built an Apache Iceberg lakehouse on S3 with AWS Glue. Used partition pruning, compaction, and type 2 slowly changing dimensions for Athena queries and QuickSight reporting.
    • Built metadata-driven ingestion with PostgreSQL, Airbyte, Airflow, and Spark, cutting data onboarding from four days to hours.
    • FastAPI
    • AWS
    • Scala
    • Spark
    • Deequ
    • Apache Iceberg
    • PostgreSQL
    • Airbyte
    • Airflow

Education

  1. Master of Information Management at University of Maryland. College Park

    • Graduate assistantship as Data Specialist.
    • Coursework: distributed systems, retrieval, applied ML, data governance.
  2. B.E. Information Technology at Panjab University, Chandigarh