Data Platform Engineer

Location: (SF / NYC / Remote — confirm preference)

Compensation: $220,000 - $250,000 + equity

Tech Stack: Python, Postgres, Clickhouse, AWS, Kafka, Spark, Airflow.

Our client is building an end-to-end content engineering platform for the AI era
. As discovery shifts from traditional search to AI-native platforms, they help brands get found—and stay found—by turning content quality into a durable competitive advantage. They’re in hyper-growth and support high-performing marketing teams at leading companies.

The team is backed by top-tier investors and operates across major hubs including San Francisco and New York (with additional global presence). Their culture emphasizes extreme ownership, quality, curiosity, and making customers heroes.

This is one of the most important technical hires our client will make this year. Customers rely on the product to understand how they appear across AI search surfaces—and the underlying data must be fast, accurate, and trusted
.

They’ve outgrown “data engineering as a side quest.” This is a foundational Data Platform role where you’ll own the customer-facing data layer end-to-end: pipelines, serving, and the guarantees that make analytics feel rock-solid.

If you want true ownership over a data product surface external users depend on—and the latitude to build it the right way—this role is for you.

Our client is hiring a Data Platform Engineer to build and scale the data systems powering customer-facing analytics like citation rates, share of voice, and mention trends across AI-driven platforms (e.g., ChatGPT, Perplexity, Gemini, and others).

This role blends product-minded engineering with deep technical execution. You’ll collaborate directly with product and engineering, moving fluidly from specs to query plans to production systems.

• Own the data pipelines powering customer-facing analytics: define what “done” means, ship it, and stand behind it
• Build the serving layer that delivers metrics with strong guarantees on accuracy, freshness, and latency
• Develop enrichment pipelines that convert raw inputs into derived entities the product depends on (classification, tagging, canonicalization, etc.)
• Partner closely with product and engineering to ship data-powered features—fast and with high quality
• Establish the data engineering foundation the team will need as the company scales (tooling, standards, performance practices, observability)

• 5+ years of hands‑on engineering experience with clear evidence you’ve owned a data‑powered product surface that external users interact with (not internal dashboards/BI‑only work)
• Strong Python and SQL
• Hands‑on experience with OLAP systems at product scale (e.g.,
  Click House, Redshift
  , or similar)
• Strong performance instincts: you know the difference between a query that works and one that holds up under real customer load
• The range to contribute to architecture decisions and still ship meaningful improvements the same week
• High ownership mentality: you optimize for outcomes, not narrow scope

• Experience at “data is the product” companies (e.g., analytics platforms, data serving products)
• Familiarity with AWS‑native stacks (Glue, S3, Redshift)
• Experience integrating LLMs into pipelines for enrichment, classification, tagging, or extraction
• Extreme Ownership
• Quality
• Curiosity and Play
• Make Our Customers Heroes
• Equity in a fast‑growing startup
• Competitive benefits package tailored to location
• Flexible time off
• Parental leave
• A fun‑loving (and slightly nerdy) team that moves fast