Principal Data Engineer
Manager
Analytics
Big Data
Business Intelligence
Data
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Integration
Data Pipeline
Data Platform
Data Processing
Data Visualization
Data Warehouse
Database
Databases
Dataops
Design
Digital Marketing
ETL
Informatica
Programming Language
Programming Languages
Reporting and Analytics
SQL
Job Description
The Principal Data Engineer will design, build, and maintain scalable data engineering solutions to support enterprise data warehousing, data lake, and analytics initiatives. The role focuses on AWS and Snowflake-based implementations, modern ETL/ELT development, and data quality and governance across complex data environments.
Key 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 solutions using AWS services including 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 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.
Preferred Qualifications
- Experience with Qlik Replicate.
- Experience with IBM InfoSphere DataStage.
- Experience with IBM CP4D.
- Experience developing and integrating APIs.
- Experience with NoSQL databases.
- Experience working with Git and DevOps practices.
- Experience in large-scale enterprise environments.
- Strong communication and cross-functional collaboration skills.
Technologies
AWS, S3, Lambda, DynamoDB, Snowflake, Python, SQL, ETL/ELT, Data Vault, dbt, Qlik Replicate, IBM InfoSphere DataStage, CP4D, APIs, Git, DevOps, NoSQL
Location and Work Arrangement
- Location: Charlotte, NC/Detroit, MI (onsite)
- Work Arrangement: Onsite
This is an onsite position that requires the ability to work from the client site on a regular basis.
Job Type
Full-Time
Work Authorization
Applicants must be authorized to work for any employer in the U.S. The company is unable to sponsor or take over sponsorship of an employment Visa at this time.
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