Sr AI Data Engineer

Job Description Summary

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.

• 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.

• 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.

• 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, Open Search, Neptune — and AI services such as Bedrock and Bedrock Knowledge Bases.
• Author infrastructure as code in Cloud Formation (CDK welcome) and apply AWS best practices for IAM, security, cost, and observability.
• Profile sources, identify data quality gaps, and design automated validation, monitoring, metadata, and lineage.

• Partner with security and platform teams to integrate data access with enterprise identity and access policies, as we look to modernize for AI.
• Define data contracts, attributes, and metadata that policy engines can reason over for attribute‑and‑context‑based access control.
• Contribute to the technical data dictionary, business glossary, and data catalog.

• Set the design direction for data and semantic modeling across the team.
• Mentor engineers and citizen developers on modeling, ontology design, and retrieval engineering.
• Communicate tradeoffs and value clearly to product, business, and executive stakeholders.

• Bachelor's degree in Computer Science, Engineering, or a STEM field with 3+ years of data engineering experience; OR high school diploma / GED with 7+ years of equivalent experience.

• Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job.

• 5+ years of hands‑on data engineering with a track record of designing — not just implementing — data models and semantic layers.
• Production experience with knowledge graphs and ontologies (Neo4j, Neptune, Tiger Graph, RDF/SPARQL, or similar) and graph query languages (Cypher, Gremlin, SPARQL).
• Strong AWS proficiency required:
  Cloud Formation (or CDK), Glue, Lambda, Step Functions, S3, IAM, Bedrock, Bedrock Knowledge Bases;
  Open Search and Neptune a plus.
• Strong Python and SQL; comfort across relational, graph, vector, and document stores.
• Experience supporting AI/ML or LLM systems — RAG pipelines, embeddings, eval datasets, grounding corpora.
• Experience integrating data access with enterprise identity and policy systems.
• Strong cross‑functional collaboration and communication, including technical presentations to non‑data audiences.

• Ability to work effectively with multi‑disciplinary teams (e.g., Digital Technology, GE Business teams) and understand the inter‑dependencies between them.
• Ability to…