Lead Data Engineer, AI & Modernization

What you'll do

• Lead data engineering work on migration engagements to build pipelines and data products for AI systems.
• Apply evidence-extraction techniques like dependency analysis and git-history mining to source estates.
• Design data supply for AI systems including vector stores, embedding pipelines, and agent-facing products.
• Manage data lineage and observability to identify and mitigate data drift before it impacts AI models.
• Engineer data foundations based on actual estate contents rather than existing documentation.
• Develop and maintain production-scale data solutions using Python and SQL.
• Build and optimize data products that feed large language model (LLM) based systems.

What we're looking for

• Minimum of 5 years of hands-on data engineering or data platform engineering experience.
• Minimum of 1–2 years of experience specifically on AI/LLM-related data work.
• Minimum of 4 years of experience working in agile engineering or consulting teams.
• Minimum of 3 years of experience with Python and SQL at production scale.
• Experience with at least one cloud data platform.
• Working fluency with data lineage and data observability.
• Minimum of 1 year designing data supply for AI systems, such as vector stores or embedding pipelines.
• Bachelor's degree, or an Associate's degree with 6 years of experience, or 12 years of work experience.
• Streaming and CDC experience (Kafka, Flink, or equivalent) (preferred).
• Code-analysis or archaeology tooling exposure (preferred).
• Experience with agentic data architecture (preferred).