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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Engineer - **Company:** Howard Hughes Medical Institute - **Location:** Chevy Chase, MD, United States - **Experience:** Experienced - **Salary:** $128,817.0 - $161,021.0 - **Contract:** Permanent contract - **Skills:** Unity 3d, Training Data, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, BigQuery, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Warehousing, Software Design Patterns, Graph Database, Identity and Access Management, Python (Programming Language), Knowledge Management, Neo4j, Operational Databases, SQL Databases, Data Streaming, Workflow Management Systems, Great Expectations (Foster Youth College-readiness and Support Program), Large Language Models, Snowflake, Apache Spark, Git, Data Layers, Data Lakes, Pyspark, Drift Detection, Entity Resolution, Terraform, Software Version Control, Data Pipelines, Databricks - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=97c5e1818487b920 ## About the Role * Hands-on production data engineering: at least four years designing, building, and operating production data pipelines. Not a Databricks-course track record - real production experience where you owned the pipeline through breakage, iteration, and recovery. * Databricks and Spark depth: Delta Lake, medallion architecture, Delta Live Tables, Workflows, Databricks SQL, Unity Catalog. Comfortable at the layer where code meets platform. * Python and SQL fluency: PySpark, ETL patterns, and SQL that runs at scale. Version control (Git), CI/CD for data pipelines, and infrastructure-as-code (Terraform) as working tools. * AI-adjacent data engineering: real experience building the data foundation for AI use cases - embedding pipelines, vector stores, chunking strategies, retrieval evaluation. Not required to be an ML researcher; required to have built the plumbing. * Workflow orchestration: Databricks Workflows, or Airflow in production. Retry semantics, dependency management, failure handling - as working discipline, not concepts. * Data quality and observability: Great Expectations, Databricks data-quality monitors, or equivalent. Treats data quality as a first-class engineering concern. * Governance discipline: works with Unity Catalog structures, understands sensitivity classification, and designs pipelines with access control and audit in mind from the first commit. * AWS foundations: IAM, S3, KMS at the level needed to work in a Databricks-on-AWS environment. Not required to be a cloud architect; required to be productive. * Communication: works productively with the K&D Architect on design, AI Developer on integration, and Operations Capabilities on contracts. Explains data-engineering trade-offs to non-engineers. * Education and experience: bachelor's degree or equivalent, plus at least four years of hands-on data-engineering experience with meaningful exposure to AI or knowledge-management use cases. Nice to Have * Prior experience with knowledge graphs (Neo4j or comparable), entity resolution, or semantic data models. * Experience with the modern data stack alongside Databricks-native tooling. * Familiarity with LLM-based extraction, chunking, and evaluation frameworks. * Background in research, academic, or mission-driven institutional environments. * Experience with cross-platform data engineering (Snowflake, BigQuery) - cross-platform judgment is useful even when Databricks is the primary tool., Remaining in a normal seated or standing position for extended periods of time; reaching and grasping by extending hand(s) or arm(s); dexterity to manipulate objects with fingers, for example using a keyboard; communication skills using the spoken word; ability to see and hear within normal parameters; ability to move about workspace. The position requires mobility, including the ability to move materials weighing up to several pounds (such as a laptop computer or tablet). ## Description The AI Accelerator exists to turn AI into daily reality across HHMI's administrative and operational functions. This role builds the data-engineering foundation every AI application and knowledge layer at HHMI runs on. The work is hands-on. This person implements the pipelines, transformation patterns, and orchestration framework that turn HHMI's institutional content into governed, AI-ready data. They own the day-to-day execution of the data-engineering side of the AI fabric - pipeline development, medallion curation, workflow-orchestration framework, and governance implementation - under the design authority of the Principal Knowledge & Data Architect. This role works closely with the Principal Knowledge & Data Architect (who owns the knowledge and retrieval layer's design), the AI Developer (who consumes what this role builds), and Operations Capabilities' Data Integration Engineer (who lands source-system data at the interface). The AI Data Engineer is the seat that connects those layers into working data flow. HHMI's Principal K&D Architect designs the knowledge and data foundation for institutional AI, but the foundation only comes to life when someone builds and operates the pipelines that carry data through it. Without this role, our AI systems either wait on data that isn't ready or consume content whose quality can't be guaranteed. This role is what makes the K&D Architect's design real day-to-day - and it's the same role that keeps it working over years, as content evolves and models change. This position works on a hybrid schedule, reporting in-person three days a week to our headquarters in Chevy Chase, Maryland. We encourage qualified candidates who are eligible to work in the United States to apply. Please note, we are not able to sponsor a visa for this position at this time. What you will do * Build the AI-facing data pipelines: ingestion from the landing zone Operations Capabilities delivers, transformation through raw bronze silver gold, and serving of governed, AI-ready content for downstream consumption. * Implement the medallion architecture: design patterns from the K&D Architect become working pipelines, tables, and materialization schedules. Delta Lake tables designed with partitioning, optimization, and evolution in mind. * Own the workflow-orchestration framework: Databricks Workflows, Delta Live Tables, retry policies, alerting routes, run history, cost tags. * Implement governance patterns: Unity Catalog structure, sensitivity classification, access control, and audit for AI-facing data assets. Design comes from the K&D Architect; day-to-day implementation lives here. * Build and operate retrieval-supporting infrastructure: embedding pipelines, vector store maintenance, reindexing when models upgrade, retrieval evaluation frameworks. * Partner with Operations Capabilities on source-system contracts: define what the AI Fabric consumes at the landing zone - schema, cadence, SLA, quality thresholds. Own the platform-side of that contract. * Design and operate data quality: data-quality checks, freshness monitoring, drift detection, and the alerting that surfaces issues before they hit AI users. * Support AI Developer velocity: when AI Developers deploy into product teams, this role is the data engineer they turn to when a use case needs specific data. * Contribute to and consume the reference-pattern library: reusable pipeline patterns, code templates, and standards live in the shared platform layer. This role uses them, contributes new ones, and evolves them as we learn., * Not a Data Platform Architect: design authority for the Databricks-on-AWS platform sits with Platform Architects. This role builds AI-facing data pipelines on that platform. * Not the Knowledge & Data Architect: the K&D Architect owns the design of the knowledge and retrieval layer; this role implements against that design. Required to be a strong hands-on builder, not an architect. * Not an AI/ML engineer: the AI Developer owns model access, orchestration, and application delivery. This role builds the data layer AI Developers consume. * Not a warehouse or BI data engineer: traditional analytics warehouses and BI-facing pipelines belong to Operations Capabilities. This role is AI-facing.