Ravindra K

About Ravindra K

Senior Data Engineer with 8+ years of experience designing, building, and maintaining scalable batch and real-time data pipelines on cloud platforms. Strong expertise in PySpark, Apache Spark, Databricks, Snowflake, and SQL, with hands-on experience across AWS and Azure.I specialize in building reliable, high-performance data platforms that support analytics, reporting, and machine learning use cases in regulated environments. My experience includes processing high-volume healthcare claims data, implementing data governance and HIPAA-compliant solutions, and delivering low-latency streaming pipelines using Kafka and Spark Structured Streaming.I have a strong background in data modeling, ETL/ELT pipeline development, performance optimization, and cloud-native deployments using Terraform, Docker, and CI/CD pipelines. I enjoy working closely with analytics, data science, and business teams to deliver analytics-ready datasets that drive data-driven decision-making.

Experience

1. Senior Data Engineer

   UnitedHealth Group

   Jul 2022 — Present

   Designed and implemented HIPAA-compliant data pipelines on Google Cloud Platform (GCP) to process large-scale healthcare datasets including claims, eligibility, provider, and member data.Built end-to-end ETL/ELT workflows using BigQuery, Cloud Dataflow (Apache Beam), Dataproc (Spark/PySpark), and Cloud Storage, enabling scalable analytics and reporting platforms.Developed real-time and batch ingestion frameworks using Pub/Sub, Kafka, and Spark Streaming, supporting near-real-time healthcare insights and operational monitoring.Implemented data quality, validation, and reconciliation frameworks to ensure accuracy and consistency of clinical and claims data across multiple downstream systems.Led on-prem to GCP migration initiatives, modernizing legacy Hadoop ecosystems into cloud-native architectures with improved performance and reduced operational cost.Built and orchestrated workflows using Cloud Composer (Apache Airflow) for dependency-driven scheduling of healthcare data pipelines.Designed secure data access models using IAM, service accounts, encryption at rest/in transit, ensuring compliance with HIPAA and internal data governance standards.Optimized BigQuery data models (partitioning, clustering, cost optimization) to support high-volume analytical workloads used by actuarial, clinical, and reporting teams.Integrated data from multiple healthcare sources including EHR systems, claims feeds, external APIs, and third-party vendors, enabling unified analytics platforms.Collaborated closely with data analysts, actuaries, clinicians, and business stakeholders to deliver trusted datasets supporting healthcare operations and regulatory reporting.