Senior Data Engineer - Applied AI

• The platform adapters that target each design partner's stack (Databricks, Snowflake, BigQuery, AWS), and the abstraction that emerges once a second stack forces it.
  
• The domain side of the evaluation and verification layer: encoding each platform's idioms and correctness rules so generated output can be trusted.
  
• The high-value generation targets with partners: ingestion and pipelines, transformation logic, warehouse and catalog scaffolding, orchestration, IaC and CI/CD, and the governance accelerators where partner pain is real (contracts, lineage, metadata, PII, data quality).
  
• The fit into real delivery: reviewable diffs, integration with partner workflows, the path to adoption.
  

Requirements

• Minimum of 5 years as a data engineer with a track record of end-to-end delivery in fast-paced environments.
  
• Have professional fluency in English , are an excellent collaborator between developers and leadership, and have the ability to guide others.
  
• Solid understanding of engineering and architectural principles , and you are comfortable collaborating on CI/CD workflows and using version control (Git)
  

• Strong production software engineering: Python, testing, CI/CD, REST, Docker. You build reusable products, not one-off pipelines.
  
• Real depth in modern cloud data platforms: You have gone deep on at least one of Databricks/Spark, Snowflake, or BigQuery, are strong in SQL and data modelling, and can reason confidently across the others and pick up a partner's stack fast.
  
• Enough AI engineering to be a full builder on an agentic system: prompt design, structured outputs, function calling, and evaluation, with the Anthropic and OpenAI APIs. You co-own the AI work, you do not just hand requirements to your AI-systems counterpart.
  
• Code-as-data fluency: codegen, templating, validating and parsing generated code, producing reviewable output.
  
• You work close to delivery teams and design partners, and you care that the thing gets used.