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Popular data-engineering skillset, metro appeal, and broad requirements lead to moderate candidate competition.
Core data engineering skills transfer across industries, but enterprise governance and domain stewardship increase specificity.
Explicit 8+ years requirement plus domain, cloud platform, and governance mandates create stringent filters.
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Lead architecture and development of domain-oriented data products leveraging EDS cloud and data platforms (Databricks, BigQuery, GCP).
Own and define business semantics, contextual data models, and embedding governance-by-design (data quality, lineage, access controls).
Provide technical mentorship and influence enterprise data platform strategy to enable scalable, trusted, AI-ready data products.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
8–12+ years of experience in data engineering, analytics engineering, or data architecture.
Hands-on experience with cloud data platforms, preferably Google Cloud Platform (GCP).
Strong expertise in data modeling, pipeline architecture, metadata, lineage, and data governance.
Experienced technical leader capable of bridging data engineering, governance, analytics, and AI teams across large-scale enterprise environments.
Strong domain stewardship skills with focus on business-aligned data products and contextual data models.
Proficient in semantic layers, knowledge graphs, and AI data enablement approaches within regulated or complex data environments.