Work Schedule
Other
Environmental Conditions
Office
Job Description

Summarized Purpose:
We are offering an opportunity for a Mid-Level Data Engineer to design, build, test, tune, and support production data pipelines using PySpark, Python, advanced SQL, AWS data services, secure data handling practices, and AI-assisted data engineering capabilities.
Education/Experience:
- Bachelor's degree or equivalent in Computer Science, Information Technology, Data Engineering, or related field
- 3-5 years of experience in data engineering, ETL development, SQL, AWS data platforms, or production data pipeline support

Major Job Responsibilities:
- Develop, test, tune, and maintain ETL and data pipelines using PySpark, Python, SQL, and AWS services
- Support ingestion and transformation of flat files, relational databases, APIs, data warehouses, and enterprise data sources
- Collaborate with business analysts, data architects, QA, DevOps, and senior engineers to implement source-to-target mappings and data solutions
- Implement CDC, incremental load design,



idempotent pipeline processing, and data reconciliation patterns for reliable data movement
- Maintain technical documentation, mapping specifications, data catalog updates, runbooks, automated tests, and release support materials

Knowledge, Skills, and Abilities:
- Hands-on experience with PySpark, Python, advanced SQL, ETL best practices, data modeling, and large-scale data processing
- Deep knowledge of Redshift performance tuning including distribution keys, sort keys, compression encoding, Spectrum, materialized views, WLM, vacuum, and analyze
- Strong knowledge of Athena optimization including partition pruning, file formats, compression, schema evolution, and cost-effective query design
- Strong understanding of DynamoDB data modeling, access-pattern-based design, capacity planning, GSIs/LSIs, TTL, Streams, and performance tuning
- Exposure to secure PHI/PII handling including encryption, access controls, auditabilit

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