Data Engineering Manager - AI & Data Modernization

Data EngineerA 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 1–2 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 1–2 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.