Vikas Padigomla

Data Engineer with 6+ years of experience building scalable data platforms and distributed data solutions using Databricks, PySpark, Apache Spark, Python, SQL, and AWS. Experienced in developing batch and streaming workflows, implementing lakehouse architectures, and processing large-scale healthcare, claims, EHR/EMR, HL7, and FHIR datasets. Strong background in Delta Lake, Snowflake, Apache Airflow, Kafka, and dbt, with expertise in data modeling, performance optimization, data quality, and HIPAA-compliant data engineering.

Proven ability to deliver reliable, analytics-ready datasets while improving pipeline performance and supporting enterprise reporting and operational analytics.

Data Engineer at UnitedHealth Group (2025-02 – Present)

• Architected Databricks lakehouse solutions using Delta Lake and PySpark to consolidate healthcare claims, EHR/EMR, HL7, and FHIR data into governed analytical datasets.
• Developed Bronze, Silver, and Gold medallion workflows with incremental processing, schema evolution, partitioning, and optimized Delta tables, improving pipeline throughput by 40%.
• Engineered real-time healthcare data ingestion using Apache Kafka and Spark Structured Streaming, reducing data latency by 45% for clinical and operational analytics.
• Built reusable dbt and SQL transformation models to create dimensional data marts and standardized datasets supporting clinical, claims, and financial reporting.
• Optimized Databricks and Snowflake workloads through SQL tuning, partitioning, clustering, caching, and resource optimization, improving query performance by 50% while reducing compute costs by 25%.
• Implemented data quality and governance controls using Great Expectations, AWS Glue Data Catalog, Python, and SQL, maintaining 99.8% accuracy and HIPAA compliance.
• Automated cloud deployments and production operations using Terraform, Docker, GitHub Actions, Jenkins, and CloudWatch, improving deployment consistency and workload monitoring.

Data Engineer at CueTech Systems (2018-07 – 2022-12)

• Developed Python and SQL ingestion frameworks to integrate structured and semi-structured data from relational databases, APIs, and file-based sources into cloud data platforms.
• Built distributed PySpark and Apache Spark applications for data cleansing, enrichment, aggregation, and transformation of high-volume enterprise datasets.
• Implemented AWS data lake solutions using S3, AWS Glue, and EMR, organizing raw and curated datasets in Parquet for downstream analytics.
• Created and maintained Apache Airflow DAGs for scheduling, dependency management, retries, monitoring, and automated execution of multi-stage data workflows, reducing processing time by 35%.
• Designed dimensional data models for Snowflake and Amazon Redshift, optimizing joins, SQL transformations, and data structures to improve analytical query performance by 40%.
• Developed Kafka-based streaming workflows for event ingestion and downstream processing, improving the availability of operational data for reporting and analytics.
• Established automated data validation and deployment practices using Python, SQL, Great Expectations, Git, Docker, and Jenkins, reducing production data issues by 45%.

Master of Science in Computer Science – Texas A&M University