Data Engineering Manager AI & Data Modernization
Data Engineer A data engineer who thinks 'governed' and 'fast' are the same requirement stated twice. You belong here even if you haven't spent years in AI. Much of what makes a great practitioner in this work was learned somewhere adjacent strong credentials in the neighboring discipline plus a year or two of serious hands-on work with LLM-based systems is exactly the profile we grow. You lead the data-engineering work on a modernization engagement: batch and streaming pipelines, productized data ready for AI consumption, and the data-plane governance catalog, quality, lineage, observability that downstream trust stands onTravel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
Here's What You Need:
Minimum of 5 years of hands-on data engineering or data platform engineering, with at least 12 years specifically on AI/LLM-related data work.
Minimum of 5 years of experience working in agile engineering or consulting teams.
Minimum of 5 years with Python and SQL at production scale, plus at least one cloud data platform.
Minimum of 5 years with streaming technologies and orchestration tooling (Kafka, Airflow, or equivalent).Minimum of 1 year designing for both AI workloads and classical analytics on the same foundation.
AI-adjacent experience counts toward the bars above: strong credentials in the neighboring discipline combined with 12 years of hands-on LLM work is exactly the profile we grow.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience).
Bonus Points If You Have:
Lakehouse/open-table-format depth (Delta, Iceberg).Data-mesh experience in decentralized organizations. Vector and graph stores alongside the relational stack.