Senior Data Engineer · Hyderabad, India
I've spent 15 years building data infrastructure and making sure the numbers leadership sees actually match what happened. That means owning the full stack pipelines that ingest and transform the data, and the Power BI and Tableau layers that deliver it. Most teams split these into two people. I do both.
Running data engineering across healthcare and finance. Snowflake platform design with Snowpipe and CDC ingestion, dbt transformation layers replacing ad-hoc SQL, Power BI governance frameworks and DAX optimisation, Tableau Server automation via Python, and AWS Glue and PySpark ETL pipelines wired together with Airflow. When a pipeline breaks at 2am, the runbook exists and the alert fires to the right person.
Healthcare data means HIPAA and SOC2 aren't checkboxes before a release, they're real audit environments with real consequences if you get it wrong. Six years building ETL pipelines on healthcare claims and eligibility data, Power BI and Tableau reporting infrastructure from scratch, and SSIS and Alteryx workflows at enterprise scale. Supported multiple audit cycles with no remediation findings on data infrastructure. That shaped how I think about compliance: architect it in from day one, don't layer it on after the fact.
| Repo | What it is |
|---|---|
| databricks-medallion-pipeline | Bronze, silver, gold architecture on Databricks, Unity Catalog governance, SCD Type 2 merge, Z-order optimization |
| adf-multi-source-ingestion | Metadata driven ADF pipeline, new sources onboard via a config row, not a pipeline rebuild |
| snowflake-dbt-pipeline | Production Snowflake and dbt healthcare data warehouse, star schema, incremental models, key-pair auth, CI/CD |
| power-bi-automation | Python automation for Power BI REST API, dataset refresh, deployment pipelines, 90 percent effort reduction |
| tableau-python-automation | Tableau Server multi-client workbook generation and publishing via Python TSC library |