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# Senior Data Engineer

- **Company:** Eliassen Group
- **Location:** Farmington, MI, United States
- **Experience:** Expert
- **Salary:** $162,240
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon S3, Cyber Security, Databases, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Security, Data Systems, DevOps, Amazon DynamoDB, JSON, Python (Programming Language), PostgreSQL, Metadata, SQL Databases, Unstructured Data, Parquet, Data Processing, Data Ingestion, Amazon Relational Database Service, Pgvector, Data Pipelines
- **Published:** November 8, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3501753732&tx=TT3936TYT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 8+ years of experience in data engineering, data integration, or related roles.
 * Hands-on experience with Python, SQL, PostgreSQL, Amazon RDS, DynamoDB, and S3.
 * Experience developing and supporting ETL/ELT pipelines and data-processing workflows.
 * Experience with data modeling, APIs, JSON, Parquet, and pgvector.
 * Understanding of data quality, metadata, governance, security, and operational monitoring.
 * Experience working within large enterprise or regulated environments.
 * Experience supporting document-processing and AI-enabled data solutions.

## Description

* Design and develop scalable data ingestion and ETL/ELT pipelines.
 * Build solutions for structured, semi-structured, and unstructured data.
 * Develop reusable data-processing and transformation components using Python and SQL.
 * Design and support data models using PostgreSQL, Amazon RDS, DynamoDB, and Amazon S3.
 * Support document ingestion and extraction using Amazon Textract, where applicable.
 * Prepare and transform data for application, search, analytics, and AI-enabled use cases.
 * Support vector-based retrieval using PostgreSQL/pgvector, where required.
 * Implement data quality, validation, reconciliation, metadata, lineage, retention, and governance controls.
 * Implement secure data access and encryption for sensitive information.
 * Optimize data pipelines and databases for performance, reliability, scalability, and cost.
 * Implement monitoring and troubleshoot data-processing and integration issues.
 * Collaborate with application, AI, DevOps, architecture, cybersecurity, and business teams.