Data Engineer
Build and own production data pipelines, knowledge graph data models, and structured datasets from messy sources (PDFs, spreadsheets, telemetry) to power internal tools, dashboards, and ML models at a frontier AI compute infrastructure company. Requires experience operating depended-on pipelines, schema modeling, data quality engineering, and unstructured data extraction.
About the job
Responsibilities
- Build the pipelines that pull every system the company runs on, ERP, ATS, project management, construction software, telemetry, into one queryable layer.
- Own the data model behind the company's live knowledge graph: entities for sites, equipment, schedules, and people that tools and agents build on.
- Ship datasets and services with SLAs that internal tools, dashboards, and ML models depend on daily.
- Turn messy vendor and field data, PDFs, spreadsheets, exports, into structured, trustworthy inputs.
Requirements
- Built and operated production data pipelines that other teams' products depended on.
- Modeled a messy real-world domain into schemas that held up as the business changed.
- Treat data quality as an engineering problem: tests, monitoring, and lineage, not spot checks.
- Done real work extracting structure from unstructured sources.
- Move fast with AI tools and modern data stacks without leaving a swamp behind.
Nice-to-Haves
- Postgres, dbt, or warehouse internals.
- Streaming and eventing.
- LLM-based extraction.
- Construction, manufacturing, or supply chain data.
Skills
Data Pipelines, Data Modeling, Data Quality, ETL, dbt, Postgres, Data Warehousing, Streaming, LLMs, SQL
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