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# Sr. Data Engineer

- **Company:** Oxenham Group LLC
- **Location:** Philadelphia, PA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Code Review, Continuous Integration, Data as a Services, Information Engineering, Data Governance, Python (Programming Language), Query Optimization, SQL Databases, Data Streaming, TypeScript, Parquet, AWS Cdk, Feature Store, Data Ingestion, Pyspark, Apache Kafka, Machine Learning Operations, Data Pipelines, Serverless Computing
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/9a95343f-bbe9-40af-97e6-d4d687a835ad/sr-data-engineer

## About the Role

Our customer is a growing health benefits company. The company is hiring a Sr Data Engineer to build the data foundation behind its cost prediction and pricing models. This is the beginning of an overarching technology modernization for a rapidly growing organization. The ideal ideal candidate has experience designing and building data pipelines and models in AWS for claims data., * 5+ years of data engineering with ownership of production pipelines and analytics platforms; hands-on ML/AI support strongly preferred

 * Advanced Python and SQL, plus production experience with AWS serverless data services; PySpark strongly preferred
 * Infrastructure as Code with AWS CDK (TypeScript)
 * Experience with streaming or event-driven pipelines (Kafka preferred) and open table formats such as Iceberg
 * Data governance and security for sensitive, regulated data (PHI, financial); healthcare claims or insurance experience strongly preferred

## Description

* Design, build, and maintain fully serverless data pipelines on AWS (Lambda, Glue, Athena, Step Functions, S3) supporting analytics, reporting, and AI use cases
 * Build versioned data models and feature stores with clear semantics, reproducible backfills, and safeguards against leakage or inconsistent definitions
 * Develop batch and event-driven ingestion pipelines (Kafka, Kinesis, or similar) for near-real-time scoring and decisioning
 * Implement automated data quality controls, monitoring, and lineage, including schema change, reconciliation, and data maturity checks
 * Partner with AI and Analytics teams on MLOps: orchestrating training and scoring workflows and capturing model and data versions to close the learning loop
 * Optimize cost and performance through Athena query tuning, columnar formats (Parquet, Iceberg), and efficient S3 partitioning
 * Enforce engineering best practices, including code review, CI/CD for data pipelines, and Infrastructure as Code with AWS CDK