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Senior Data Engineer
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Senior Data Engineer
Virtusa- Posted 14 days ago
- Over 50 applicants have applied
Job Description
Job Requirements
Role Overview
The Senior Data Engineer will build trusted real-time and historical data products for the Real-Time Finance Close and Value-Based Care platform. You will design streaming and batch pipelines integrating claims, encounters, eligibility, provider rosters, capitation, subledger, and GL data. The core objective is preserving financial/operational history, managing late-arriving and retroactive changes, and enabling point-in-time point reproducibility for accounting, actuarial analysis, Power BI reporting, Cube.dev semantic layers, and AI analytics.
Key Responsibilities
Pipeline Engineering: Design and deploy reliable batch, event-streaming, and CDC ingestion pipelines with automated patterns for schema evolution, replay-safety, quarantine, and reprocessing.
Streaming & Data Modeling: Build stateful/stateless transformations handling late events, watermarks, reattributions, contract amendments, and claim adjustments. Implement dimensional, Data Vault, lakehouse, or effective-dated/bitemporal models.
Data Quality & Controls: Implement automated control totals, reconciliation, and audit controls to guarantee completeness, validity, and line-of-business integrity across GL balances and data products.
Semantic & Platform Enablement: Deliver curated datasets optimized for Cube.dev, Power BI, and AI tools. Standardize business definitions (PMPM, IBNR, MCR, capitation). Enforce RBAC, encryption, masking, and PHI compliance.
Mandatory Qualifications
5+ years of data engineering experience with advanced SQL and Python, Scala, or Java.
Hands-on experience with Kafka and distributed processing engines (Spark, Flink, Beam).
Experience building batch/streaming pipelines on cloud platforms, lakehouses, or data warehouses.
Proficiency in dimensional modeling, data partitioning, incremental processing, and performance tuning.
Experience with orchestration, CI/CD, data-quality testing, production monitoring, and access controls.
Preferred Qualifications
Domain knowledge in healthcare claims, eligibility, capitation, sub-capitation, attribution, or value-based care.
Experience with financial accounting datasets (journals, subledgers, GL balances, reconciliations).
Familiarity with healthcare standards (X12 837/835, HL7, FHIR), dbt, Cube.dev, Power BI, and knowledge graphs/ontologies.
Work Experience
5-7Years
Role Overview
The Senior Data Engineer will build trusted real-time and historical data products for the Real-Time Finance Close and Value-Based Care platform. You will design streaming and batch pipelines integrating claims, encounters, eligibility, provider rosters, capitation, subledger, and GL data. The core objective is preserving financial/operational history, managing late-arriving and retroactive changes, and enabling point-in-time point reproducibility for accounting, actuarial analysis, Power BI reporting, Cube.dev semantic layers, and AI analytics.
Key Responsibilities
Pipeline Engineering: Design and deploy reliable batch, event-streaming, and CDC ingestion pipelines with automated patterns for schema evolution, replay-safety, quarantine, and reprocessing.
Streaming & Data Modeling: Build stateful/stateless transformations handling late events, watermarks, reattributions, contract amendments, and claim adjustments. Implement dimensional, Data Vault, lakehouse, or effective-dated/bitemporal models.
Data Quality & Controls: Implement automated control totals, reconciliation, and audit controls to guarantee completeness, validity, and line-of-business integrity across GL balances and data products.
Semantic & Platform Enablement: Deliver curated datasets optimized for Cube.dev, Power BI, and AI tools. Standardize business definitions (PMPM, IBNR, MCR, capitation). Enforce RBAC, encryption, masking, and PHI compliance.
Mandatory Qualifications
5+ years of data engineering experience with advanced SQL and Python, Scala, or Java.
Hands-on experience with Kafka and distributed processing engines (Spark, Flink, Beam).
Experience building batch/streaming pipelines on cloud platforms, lakehouses, or data warehouses.
Proficiency in dimensional modeling, data partitioning, incremental processing, and performance tuning.
Experience with orchestration, CI/CD, data-quality testing, production monitoring, and access controls.
Preferred Qualifications
Domain knowledge in healthcare claims, eligibility, capitation, sub-capitation, attribution, or value-based care.
Experience with financial accounting datasets (journals, subledgers, GL balances, reconciliations).
Familiarity with healthcare standards (X12 837/835, HL7, FHIR), dbt, Cube.dev, Power BI, and knowledge graphs/ontologies.
Work Experience
5-7Years
More Info
Key Skills
Flink
CI CD
Beam
incremental processing
access controls
Cube.dev
data-quality testing
data partitioning
About Company
Skills:
Git, Apache Spark, Databricks, Sql, Python, AWS, dbt
Skills:
Agile and DevOps practices, GCP Cloud Composer, GCP Dataflow, GCP data engineering, Shell scripting or Python, GCP BigQuery, Relational databases RDBMS, Advanced SQL PLSQL scripting, Data processing and workflow orchestration, GCP Dataform
Skills:
monte carlo , snowflake , Sql, ELT, Git, Azure, Python, Etl, Atlan, dbt, CI/CD, Fivetran, Immuta
Skills:
Numpy, Pandas, Tableau, Sql, Python
Skills:
Pyspark, Data Modeling, ELT, Cloud Storage, Numpy, Docker, Terraform, Python, BigQuery, Sql, Jenkins, Pandas, Spark, Kubernetes, Etl, GCP Core Services, Infrastructure as Code, Cloud Dataflow, Real-time streaming architectures, CI CD, Pub Sub, Cloud Run, GitHub Actions, API integrations, Cloud Functions, Cloud Dataproc, Cloud Composer Airflow