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Infrastructure and data

Data Platform Engineer

This role owns the data layer that the product depends on: ingestion, storage, transformation, and query performance. You will design systems that handle high-volume AI output reliably and give the product the data quality it needs to report with confidence.

Applications opening soon

Role summary

Build and maintain the data infrastructure that makes AI visibility analysis reliable, fast, and scalable as prompt volume and reporting complexity grow.

Why this role exists

Data volume is increasing faster than the current infrastructure was designed for. We need someone to build a platform that can grow with the product without becoming a constant maintenance burden.

First 90 days

Map the current data flow end to end and identify the top three reliability or performance gaps.

Why this role exists

Data volume is increasing faster than the current infrastructure was designed for. We need someone to build a platform that can grow with the product without becoming a constant maintenance burden.

What you will work on

  • Design and operate data pipelines from AI prompt execution through to query-ready reporting tables.
  • Own storage, schema design, and query optimization for high-volume, time-series AI output data.
  • Build internal tooling that helps the research and product teams explore and validate data quality.
  • Establish data observability practices so problems are caught before they reach the product.

What a strong fit looks like

  • Strong experience designing data pipelines and schemas for analytical workloads.
  • Comfort with time-series data, partitioning strategies, and query performance at volume.
  • Experience building and operating pipelines that handle partial failures, late data, and schema evolution.
  • A product mindset: you understand that bad data leads to bad product decisions.

What will excite you here

  • Building the data foundation for a product category that does not have an established playbook yet.
  • Owning the full data platform, not just one pipeline.
  • Working on infrastructure where quality directly determines whether the product can be trusted.

First 90 days

  1. 01Map the current data flow end to end and identify the top three reliability or performance gaps.
  2. 02Ship at least one pipeline improvement that reduces latency or error rate on a core data path.
  3. 03Establish basic data observability so the team has visibility into pipeline health.

Hiring process

The process is intentionally short, direct, and anchored in the real work.

  1. 1

    Apply

    Send us your background, relevant work, and why this role makes sense for you.

  2. 2

    Foundational conversation

    A focused conversation about your work, your judgment, and the role itself.

  3. 3

    Role-specific deep dive

    A discussion or exercise that looks like the actual work more than a generic interview loop.

  4. 4

    Founder conversation

    A final conversation on standards, ambition, and what success would look like here.

  5. 5

    Decision

    We close the loop clearly and move quickly once there is conviction.

Need context before you apply? [email protected]

Data Platform Engineer

We are not taking applications for this role yet. We will update this page when it opens.

Questions in the meantime? Email [email protected].