Must Have Technical/Functional Skills
Production data engineering at scale: SQL, Python, dbt, incremental processing/CDC, schema evolution, data quality
Reusable, metadata-driven or configurable ingestion, transformation, quality and delivery frameworks
Dimensional/analytical data modeling and lakehouse architecture with Delta Lake or Apache Iceberg
Semantic and knowledge architecture: semantic models, metric layers, ontologies, knowledge graphs, vector search
Applied AI in production: retrieval, conversational analytics, agent-based capabilities with evaluation and traceability
Roles & Responsibilities
Shape data and AI architecture from business problem through production
Build reusable capabilities that other engineers adopt
Take retrieval, conversational analytics and agent-based solutions beyond prototypes
Deliver secure, maintainable solutions: access controls, tenant/client isolation, CI/CD, monitoring, lineage
Code alongside engineers, review implementation and make reuse-vs-build decisions
Generic Managerial Skills, If any
Director-level technical leadership
Turn ambiguous consulting and product needs into a practical engineering approach
Guide teams and raise the team's technical capability
Challenge thinking and introduce new approaches
Stay accountable for the domain solution and explain tradeoffs made