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Base Power

Data Engineer

Austin, TXfulltimemidAdded 3 weeks ago

About this role

Base is seeking a Data Engineer to design and operate backend data infrastructure for a distributed battery network transforming the power grid. You'll own end-to-end data pipelines, systems, and platforms that ingest telemetry from thousands of batteries, market signals, and operational systems, making data reliable and accessible across the company.

What you'll do

  • Build and maintain backend systems that ingest, transform, and serve data across the company
  • Design and operate reliable batch and streaming data pipelines end-to-end
  • Develop data models and schemas for high-volume time-series and event data
  • Manage ingestion from diverse sources including IoT telemetry, APIs, and operational systems
  • Build platforms for cross-functional teams to query and visualize data
  • Ensure data quality, consistency, and governance across source-of-truth datasets

What they're looking for

  • 5+ years data engineering or backend engineering with data focus
  • Data infrastructure experience (BigQuery, Snowflake, Redshift, Databricks)
  • Python and SQL proficiency
  • Production pipeline orchestration (Airflow, Prefect, dbt)
  • Multi-source data integration (APIs, streams, databases)
  • Cloud infrastructure and infrastructure-as-code (GCP/AWS, Terraform)
  • Schema design and data quality practices
  • Go or compiled language experience (preferred)
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Base Power

Base Power develops high-power-density battery energy storage systems and related hardware for residential and commercial energy applications. The company is hiring System Design, Thermal, and Mechanical Engineers for product development, plus Supply Chain and Data Engineers to build internal platforms and infrastructure supporting distributed battery operations at scale.

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Likely interview questions

  • Walk us through a data pipeline you've built end-to-end—what sources did you integrate, what transformations did you apply, and how did you ensure reliability in production?
  • Describe your experience designing schemas for high-volume time-series or event data. How did you balance query performance, storage efficiency, and scalability?
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