Sr AI Data Engineer

GE Aerospace

7d • $95,000 - $159,000 • Remote

About The Position

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.

Requirements

• 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.
• Strong AWS proficiency required: CloudFormation (or CDK), Glue, Lambda, Step Functions, S3, IAM, Bedrock, Bedrock Knowledge Bases; OpenSearch 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.

Nice To Haves

• 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, TigerGraph, RDF/SPARQL, or similar) and graph query languages (Cypher, Gremlin, SPARQL).
• Leadership Skills: Ability to work effectively with multi-disciplinary teams (e.g., Digital Technology, GE Business teams) and understand the inter-dependencies between them.
• Ability to showcase teamwork skills to achieve common goals, provide resolutions and share ideas.
• Demonstrate the presentation and influencing skills.

Responsibilities

• 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, OpenSearch, Neptune — and AI services such as Bedrock and Bedrock Knowledge Bases.
• Author infrastructure as code in CloudFormation (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.

Benefits

• Healthcare benefits include medical, dental, vision, and prescription drug coverage; access to a Health Coach from GE Aerospace; and the Employee Assistance Program, which provides 24/7 confidential assessment, counseling and referral services.
• Retirement benefits include the GE Aerospace Retirement Savings Plan, a 401(k) savings plan with company matching contributions and company retirement contributions, as well as access to Fidelity resources and planning consultants.
• Other benefits include tuition assistance, adoption assistance, paid parental leave, disability insurance, life insurance, and paid time-off for vacation or illness.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior