The Principal Data Engineer designs, develops, and maintains scalable data solutions for enterprise data engineering and analytics initiatives. The scope covers data engineering, data warehousing, data integration, and cloud-based platforms, with hands-on work across AWS and Snowflake.
Role Focus
In this onsite role based in Detroit, MI, you will build and optimize data pipelines, integration solutions, and analytics-ready data architectures. Responsibilities include ETL/ELT development, data modeling (including Data Vault), orchestration with dbt, and ensuring data quality and governance across complex environments.
Responsibilities
- Design, develop, and optimize scalable data pipelines and data integration solutions.
- Develop and maintain data architectures for enterprise data warehouses, data lakes, and analytics platforms.
- Build and optimize ETL/ELT processes using modern data engineering tools and technologies.
- Develop AWS-based solutions using S3, Lambda, and DynamoDB.
- Design and implement data solutions within Snowflake and other cloud-based data environments.
- Develop and maintain data models, including Data Vault modeling methodologies.
- Write and optimize complex SQL and Python code for data processing and integration.
- Use dbt to develop, transform, test, and manage data workflows.
- Support data replication and integration using tools such as Qlik Replicate.
- Work with enterprise data platforms including IBM InfoSphere DataStage and CP4D.
- Develop and integrate APIs to support enterprise data and application needs.
- Establish and maintain data quality, governance, metadata management, and data lineage processes.
- Collaborate with engineering, architecture, analytics, and business teams to translate requirements into scalable data solutions.
- Troubleshoot performance, data quality, and integration issues across complex data environments.
- Provide technical leadership and guidance on data engineering architecture and best practices.
Required Skills and Qualifications
- Strong experience in data engineering and enterprise data environments.
- Hands-on experience with AWS, particularly S3, Lambda, and/or DynamoDB.
- Strong experience with Snowflake and cloud data warehousing.
- Advanced Python and SQL development skills.
- Experience developing ETL/ELT and data integration solutions.
- Experience with data warehousing and data modeling, including Data Vault.
- Experience with dbt or similar modern data transformation frameworks.
- Experience with data quality, governance, metadata management, and data lineage.
- Strong understanding of relational and non-relational databases.
- Experience with enterprise data integration platforms and tools.
- Ability to work independently while providing technical leadership to other engineers.
Technologies
AWS, S3, Lambda, DynamoDB, Snowflake, Python, SQL, ETL/ELT, dbt, Qlik Replicate, IBM InfoSphere DataStage, CP4D, Data Vault, APIs.
Location and Work Arrangement
- Location: Charlotte, NC / Detroit, MI
- Work arrangement: Onsite
- Onsite requirement: Ability to work from the client site on a regular basis
Job Type
Full-Time
