As a Principal Data Engineer within the Prime & GTS team, you will play a pivotal role in shaping our data ecosystem. You will serve as the subject matter expert (SME) for data analysis and data element design, ensuring that data is accurately modeled, mapped, and integrated across systems. Your work will directly influence how data is manufactured, consumed, and trusted across the organization.
As part of the Prime & GTS team, you’ll:
- Act as the Principal Data Analyst SME, advising on data element creation, mapping, corrections, and operational data issues.
- Lead data analysis efforts to understand data flows, transformations, and dependencies across systems.
- Collaborate with business units and IT teams to ensure accurate data integration and transformation.
- Review and refine data element requirements, ensuring alignment with existing architecture and minimal disruption.
- Serve as a trusted advisor on data architecture, collaborating closely with engineering teams.
- Communicate effectively with stakeholders, translating complex data concepts into actionable insights.
- Guide and mentor team members on best practices in data analysis and engineering.
About you:
- Minimum 10+ years of experience in data analysis, with a strong background in developing data schemas and engineering data elements.
- Proven expertise in SQL, and working with structured and semi-structured data formats such as XML and JSON.
- Deep understanding of columnar database architectures and performance optimization techniques.
- Extensive experience with Big Data platforms and technologies, including ETL pipelines, and a comprehensive grasp of the data lifecycle—from ingestion and cleansing to transformation, enrichment, and packaging.
- At least 6+ years of hands-on experience in Data Engineering, including building and maintaining Data Warehouses, Data Lakes, and real-time data streaming solutions.
- Proficiency with cloud platforms such as Google Cloud Platform (GCP) and Amazon Web Services (AWS).
- Demonstrated experience working in Agile environments as part of a data platform or engineering team.
- Hands-on experience with modern data engineering tools and environments such as Amazon Redshift, Snowflake, and Google BigQuery, ideally at enterprise scale.
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