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Senior Data Engineer
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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:
BigQuery, Pyspark, Dataproc, Sql, Cloud Storage, Git, Terraform, DataFlow, Solution Architecture, Python, Airflow, Cloud Composer, CI/CD, Pub/Sub
Skills:
snowflake , Devops, Git, Python, Sql, dbt, CI CD
Skills:
Java, Apache Airflow, Scala, Apache Spark, Sql, Apache Nifi, Cloud-native data services, Object Storage platforms, Hadoop ecosystem technologies
Skills:
Pyspark, Databricks, Data Modeling, Python, Sql, Etl, Delta Lake, Data pipeline orchestration
Skills:
Elasticsearch, Sql, ELT, Etl, Java, Python, Kinesis, Kafka, Data Modeling, Airflow, OpenSearch, Dagster