Narsa Reddy
Data Engineer | AWS · Snowflake · Airflow · Python | ETL & Cloud Pipelines | Healthcare & Fintech | Open to New Opportunities
- Role
- Data Engineer at The Cigna Group
- Location
- Dallas, TX, US
- LinkedIn followers
- 500 followers
About Narsa Reddy
Data Engineer with 3+ years of experience building production-grade ETL pipelines and scalable cloud data solutions in healthcare and fintech. I turn messy, complex datasets into clean, reliable data products that directly drive business decisions. Tech Stack: Python · SQL · AWS (S3, Glue, Lambda, Redshift) · Snowflake · Apache Airflow · Apache Spark · dbt · FastAPI What I\'ve delivered:• At Cigna, designed Airflow & Python ingestion pipelines transforming HL7, CCD, and JSON datasets into Snowflake tables supporting cost modeling for 5M+ members — reducing data errors by 35% and achieving 99% accuracy through automated SQL validation.• At Citi, engineered data integration solutions for transactional and compliance systems across global financial and healthcare domains — cutting data latency by 40% through decoupled ETL architectures.• Built an AI-Driven Data Compliance Platform using Python, FastAPI, and PostgreSQL for real-time compliance tracking, and a Healthcare Data Quality Framework using Airflow, AWS Glue, and Snowflake with dbt transformations. M.S. in Technology Management — Lindsey Wilson University (GPA 3.9/4.0)Currently open to Data Engineer roles focused on cloud pipelines, real-time streaming (Kafka), and healthcare or fintech data products. Let\'s connect!
Experience
Data Engineer
May 2025 — Present · TX, US
Designed and deployed Airflow & Python-based data ingestion pipelines to transform HL7, CCD, and JSON healthcare datasets into Snowflake tables — supporting cost modeling for 5M+ members across the enterprise. Automated data quality validation using SQL + Python scripts, reducing data pipeline errors by 35% and achieving 99% ingestion accuracy across all data layers. Implemented AWS real-time workflows (S3, Lambda, Glue) for continuous data monitoring, cutting data freshness latency from daily batch to <15 minutes. Partnered with the Data Science team to develop Apache Spark distributed processing frameworks for utilization analytics — reducing processing time from 6 hours to 45 minutes (87% improvement). Presented architecture reviews to engineering and leadership teams, advocating for data governance best practices and scalable pipeline design.
Education
Lindsey Wilson University
Master of Science - MS, Technology Management
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