Data Engineer

Fluidstack

$100K — $150K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years experience building and operating production data pipelines
  • Proven ability to model complex domains into scalable schemas
  • Strong commitment to data quality through engineering processes
  • Experience extracting structured data from diverse unstructured sources
  • Familiarity with modern AI tools and data stacks including Postgres or dbt in a fast-paced environment
  • Bonus experience with streaming data and LLM-based data extraction techniques

Responsibilities

  • Develop pipelines integrating various operational systems into a unified data layer
  • Manage the data model for the company's knowledge graph involving essential entities
  • Deliver datasets and services aligned with service level agreements for internal tools
  • Transform disorganized vendor and field data into structured formats for analysis
  • Ensure reliable and trustworthy data inputs for machine learning models and dashboards

Benefits

  • Commitment to pay equity and transparency
  • Possibility of tailored recruitment for exceptional candidates beyond the outlined role requirements
Full Job Description
Role Scope
  • 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.
What We're Looking For
  • The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.
  • You've built and operated production data pipelines that other teams' products depended on.
  • You've modeled a messy real-world domain into schemas that held up as the business changed.
  • You treat data quality as an engineering problem: tests, monitoring, and lineage, not spot checks.
  • You've done real work extracting structure from unstructured sources.
  • You move fast with AI tools and modern data stacks without leaving a swamp behind.
  • Bonus: Postgres, dbt, or warehouse internals. Streaming and eventing. LLM-based extraction. Construction, manufacturing, or supply chain data.


We are committed to pay equity and transparency.

You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email [redacted] with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.

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