Data Engineer
Senior Data Engineer at Virtusa (Hyderabad/Chennai/Bengaluru) building real-time and batch data pipelines for a Real-Time Finance Close and Value-Based Care platform, integrating claims, eligibility, GL, and actuarial data. Core stack: Python, PySpark, SQL, Snowflake, Databricks, and Kafka/Spark streaming.
- Compensation
- Not disclosed
- Location
- Hyderabad; Chennai; Bengaluru
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Python, PySpark and SQL
- Snowflake
- Medallion Architecture
- Databricks
- 6 or more years of data engineering experience
- Advanced SQL skills; experience with Python, Scala, Java, or another data engineering language
- Hands-on experience with distributed processing frameworks such as Spark, Flink, Beam, Kafka Streams, or equivalent
- Experience with Kafka or another event-streaming platform
- Experience building both batch and streaming data pipelines
- Experience with a cloud data platform, lakehouse, data warehouse, or distributed storage environment
- Strong understanding of dimensional modeling, data partitioning, incremental processing, and performance optimization
- Experience with data orchestration, automated deployment, and version-controlled data transformation
- Experience implementing data-quality tests and production data monitoring
- Understanding of identity, access controls, sensitive-data handling, and secure data engineering
Full description
Senior Data EngineerPrimary Skills:
- Python, PySpark and SQL
- Snowflake
- Medallion Architecture
- Data Bricks
Position summary
The Senior Data Engineer will build trusted real-time and historical data products for the Real-Time Finance Close and Value-Based Care platform.
The role will integrate claims, encounters, member eligibility, provider, contract, capitation, sub-capitation, actuarial, journal, subledger, GL, payment, and operational data. The engineer will create streaming and batch pipelines that support financial processing, actuarial analysis, reconciliation, Cube.dev semantic models, Power BI reporting, ontology integration, and AI-driven analytics.
The engineer must be able to preserve financial and operational history, correctly process late and retroactive changes, and reproduce what the organization knew at a particular accounting or valuation date.
Key responsibilitiesData ingestion and integration
- Build reliable ingestion pipelines for:
- Claims and claim lines
- Clinical encounters
- Eligibility and enrollment
- Member attribution
- Provider rosters and hierarchies
- Payer and provider contracts
- Capitation and sub-capitation
- Remittances and payments
- Risk adjustment
- Quality and performance measures
- Actuarial model outputs
- Journals, subledgers, and GL balances
- Support event streaming, APIs, change-data capture, files, databases, and partner interfaces.
- Develop reusable patterns for schema evolution, quarantine, reprocessing, and source-system reconciliation.
Streaming and transformation engineering
- Develop stateful and stateless streaming pipelines.
- Correctly handle:
- Event time versus processing time
- Late-arriving data
- Out-of-order events
- Duplicate events
- Windowing and watermarks
- Retroactive eligibility changes
- Reattribution
- Contract amendments
- Claim reversals and adjustments
- Create incremental, restartable, and replay-safe transformations.
- Maintain control totals and processing checkpoints.
Healthcare and financial data modeling
- Design data products for:
- Member-month populations
- Contract and risk arrangement
- Inbound capitation
- Outbound sub-capitation
- Provider settlement
- FFS claims and encounters
- Medical-cost and utilization analysis