FluidStack
Data Engineer
About this role
Fluidstack seeks a Data Engineer to build production data pipelines that consolidate company-wide systems into a unified, queryable layer supporting internal tools and ML models. You'll own the data models and knowledge graph powering the organization's infrastructure operations, transforming messy vendor and field data into reliable, trustworthy datasets with SLAs.
What you'll do
- Design and operate production data pipelines integrating ERP, ATS, project management, construction software, and telemetry systems
- Own the data model and live knowledge graph for sites, equipment, schedules, and people entities
- Ship datasets and services with defined SLAs that internal tools, dashboards, and ML models depend on daily
- Transform unstructured data sources—PDFs, spreadsheets, vendor exports—into structured, validated inputs
- Implement data quality practices including tests, monitoring, and lineage tracking
- Collaborate with internal teams to evolve schemas as business needs change
What they're looking for
- Production data pipeline design and operation
- Data modeling and schema design
- SQL and database systems (Postgres experience preferred)
- dbt or equivalent transformation tools
- Data quality, testing, and monitoring practices
- Unstructured data extraction and structuring
- Cloud data warehousing
- LLM-based extraction (bonus)
Opens the official application on the employer’s site. No login required.
FluidStack
FluidStack builds AI infrastructure at scale, developing data centers and warehouse operations designed to handle gigawatt-capacity compute deployment. The company is hiring for warehouse engineers, data center operations specialists, product engineers, and people leaders to support rapid infrastructure expansion across multiple sites.
- Website
- fluidstack.io
Likely interview questions
- Walk us through a production data pipeline you built where other teams depended on its reliability—what challenges did you face and how did you ensure quality?
- Describe a time you modeled a complex, messy real-world domain into a schema. How did you handle evolving business requirements?