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Data Engineer

Job description


Data Engineer (Q4 2025 hire)

Build and maintain ETL pipelines that process contracts and invoices at scale with high data quality

Technical Delivery Requirements

Data Pipeline Architecture

  • ETL pipelines for contract ingestion: PDF/DOCX → structured data
  • Invoice processing pipeline: CSV/Excel/PDF → validation queue
  • Historical data migration for new customers (10K+ invoices per health system)
  • Real-time data streaming for webhook integrations
  • Data quality monitoring and validation

Data Warehouse

  • Build analytics data warehouse for reporting
  • Aggregate validation metrics, savings data, exception patterns
  • Customer-specific data models for ROI reporting
  • Time-series data for trend analysis
  • Support for 245+ contract/validation schema fields

Data Quality & Governance

  • Data validation: completeness, accuracy, consistency checks
  • PII/PHI detection and masking
  • Data lineage tracking: source → transformation → destination
  • Error handling and dead letter queues
  • Data retention policies (5-year audit trail)

Required Skills

  • ETL tools: Airflow, Prefect, or similar for workflow orchestration
  • Python: Data processing libraries (pandas, numpy, pyspark)
  • SQL mastery: PostgreSQL, data modeling, query optimization
  • Data warehousing: Redshift, BigQuery, or Snowflake
  • Streaming: Kafka, Kinesis for real-time data processing
  • Data quality: Great Expectations or similar frameworks

Key Deliverables (Next 6 months)

  • Build ETL pipelines for 9 customer environments
  • Process 100K+ historical invoices for new customers
  • Achieve 99%+ data pipeline reliability
  • Reduce data processing time by 30%
  • Build analytics warehouse for reporting module

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