Sr AI Data Engineer

Job Description Summary

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The Senior Data Engineer designs and builds the AWS-native data foundation behind our enterprise AI applications — knowledge graphs, semantic layers, retrieval corpora, and the pipelines that keep them trustworthy. This role leads both the design strategy for how our AI systems understand enterprise data and the hands-on engineering to make it real. You will set the patterns the rest of the team — including citizen developers building with Agents and MCPs — follow when they access, curate, or extend our data.Job Description ===================

Roles and Responsibilities

Knowledge Graph and Semantic Layer (primary focus)

• Lead the design and evolution of the knowledge graphs and ontologies powering our AI's reasoning, retrieval, and explainability.
• Align enterprise data (engineering handbooks, parts, service manuals, DMAIC records, user files) into a coherent, queryable graph with clear provenance across structured, semi-structured, and unstructured sources.
• Own the retrieval substrate — graph queries, vector indexes, and hybrid retrieval — and drive measurable improvements in grounding quality.

AI/ML Data Quality

• Curate grounding corpora, eval datasets, and retrieval benchmarks for LLM-based features.
• Instrument metrics for retrieval quality, grounding accuracy, and freshness; drive regressions down over time.
• Shape training and inference data contracts with AI engineers, including feedback loops from user signals.

Data Modeling and Pipelines on AWS

• Produce conceptual, logical, and physical data models for operational and analytical workloads; establish modeling standards, naming conventions, and reuse patterns.
• Build ingestion and transformation pipelines in Python and SQL using AWS services — Glue, Lambda, Step Functions, S3, Athena, OpenSearch, Neptune — and AI services such as Bedrock and Bedrock Knowledge Bases.
• Author infrastructure as code in CloudFo