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Serwes Bhatta

@serwesbhatta

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Senior data engineer building secure, scalable cloud data platforms.

United States

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What I'm looking for

I’m looking for a cloud-first data engineering role where I can lead secure, governed, high-performance batch and streaming pipelines—partnering with analytics, security, and compliance to deliver production-scale platforms and measurable cost/performance wins.

I’m a Senior Data Engineer with over 7 years designing and delivering high performance, scalable data solutions across healthcare, insurance, and retail. I focus on production-ready enterprise data platforms that turn complex data into reliable insights.

I build and optimize large-scale pipelines using both batch and streaming frameworks, including Apache Spark, Kafka, and Apache Airflow—delivering maximum performance and reliability through ETL and ELT. I specialize in cloud-first architectures, with hands-on execution across Azure (ADF, Synapse, Databricks, Purview, Event Hubs) and GCP (BigQuery, Cloud Storage).

Security, compliance, and governance are central to my work—especially for sensitive healthcare and financial data in alignment with HIPAA and enterprise standards. I lead implementations using Azure Key Vault, Azure RBAC, Azure Policy, and metadata management/lineage tooling (Azure Purview; also Google Cloud Data Catalog), ensuring high-quality, accessible, compliant data.

I’ve repeatedly driven measurable outcomes: $500,000 in annual savings, $350,000 cloud cost reductions, and improvements in reporting latency, operational costs, and data accuracy. I enjoy collaborating across analytics, security, and compliance teams, and I also mentor others during cloud modernization and delivery.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Guardian Life Insurance

Apr 2024 - Present (2 years 2 months)

Designed and implemented enterprise Azure data pipelines for EHR, claims, and patient engagement, driving $500,000 annual savings through improved processing times and reporting accuracy. Built secure, compliant streaming and warehousing capabilities (HIPAA), including Azure Event Hubs/Stream Analytics and Snowflake marts that reduced reporting latency and saved $75,000 in operational costs.

Azure Data Factory Azure Databricks Azure Event Hubs Azure Stream Analytics Snowflake Pyspark Great Expectations

Data Engineer

Kaiser Permanente

Oct 2022 - Mar 2024 (1 year 5 months)

Built data ingestion and transformation pipelines with Snowflake, Databricks, and GCS, improving reporting efficiency by $250,000 annually. Implemented real-time processing with Kafka/Databricks and automated quality/governance with dbt and Great Expectations, delivering $500,000 annual savings via faster decision-making.

Snowflake Databricks Apache Kafka Google Cloud Storage DBT Great Expectations Pyspark SQL Apache Airflow Terraform

Data Engineer

Johnson & Johnson

May 2021 - Aug 2022 (1 year 3 months)

Delivered scalable Azure-native data pipelines using ADF, ADLS Gen2, Databricks, Synapse, and Azure SQL, supporting clinical/regulatory workloads under FDA/HIPAA constraints. Built optimized ETL/ELT workflows and Medallion architecture with PySpark and streaming ingestion via Event Hubs/Stream Analytics, improving reliability through monitoring, CI/CD, and production support.

Azure Data Factory Azure Databricks Azure Synapse Azure Event Hubs Azure Stream Analytics Pyspark T SQL Azure DevOps

Big Data Developer

Nike, Inc.

Aug 2019 - Apr 2021 (1 year 8 months)

Developed large-scale Hive and Hadoop ETL pipelines to support Nike retail analytics, improving reporting performance through optimized HiveQL, UDFs, and distributed MapReduce jobs. Built robust ingestion and scheduling using Sqoop, Oozie, Flume, and scripts, including data validation and lineage with Atlas to ensure reliable governance and faster batch processing (40% reduction).