Nikhil Teja Sirikonda

About Nikhil Teja Sirikonda

Data engineer solving messy healthcare data problems at scale. I build cloud platforms that process 10M+ Medicare claims monthly—turning CMS CCLF files, BCDA data, and EHR feeds into analytics that clinical and finance teams actually trust. What I build: Medallion architectures on Azure/Databricks/Snowflake, production dbt models for HEDIS measures and RAF scoring, HIPAA-compliant data pipelines with 99%+ uptime. What I\'m good at: Taking complex healthcare data standards (CMS claims, HL7, FHIR) and making them useful. Optimizing slow queries. Cutting cloud costs while improving performance. Translating between technical and clinical teams.Background: 3+ years in healthcare data engineering, MS in Data Science from Rowan University. Currently building data infrastructure for FFS and Medicare Advantage analytics.

Experience

1. Senior Data Support Engineer

   Early Read

   Sep 2023 — Present · US

   Built distributed data pipelines processing 10M+ healthcare claims per month using Azure Data Factory, Databricks (PySpark), Synapse Analytics, ADLS Gen2, and SnowflakeDeveloped Python ingestion framework including an HL7 parser, reducing manual data preparation by 40% and standardizing clinical feeds across multiple data sourcesDesigned medallion architecture (bronze/silver/gold) and dimensional data models powering 10+ Power BI dashboards used by leadership and operations teamsImplemented dbt transformations with incremental processing, SCD Type 2 snapshots, and automated data quality tests ensuring high data accuracyImproved query performance by 2x and reduced compute costs by 20% through partitioning, clustering, and caching strategiesBuilt CI/CD pipelines using GitHub Actions and Azure DevOps, reducing deployment time and improving release stabilityImplemented HIPAA-aligned security controls using Azure Entra ID RBAC, Azure Key Vault for secrets management, and Snowflake dynamic data masking for PII protectionDeployed Azure Batch compute pools with static IPs and subnet isolation supporting secure large-scale processing of protected health informationImplemented comprehensive monitoring using Azure Monitor and Log Analytics with custom alerts for proactive pipeline health trackingContributed to LLM-powered analytics using Snowflake Cortex Analyst and optimized semantic layer configuration

Education

• Rowan University

  Master of Science, Data Modeling/Warehousing and Database Administration