Senior Staff Engineer, Data Platform

Data Platform Engineer (Customer-Facing Analytics)

Location: (SF / NYC / Remote confirm preference)
Compensation:$220000 - $250000 equity

Tech Stack: Python Postgres Clickhouse AWS Kafka Spark Airflow.

About Our Client

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 foundand stay foundby turning content quality into a durable competitive advantage. Theyre 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.

Why This Role Why Now

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 surfacesand the underlying data must be fast accurate and trusted.

Theyve outgrown data engineering as a side quest. This is a foundational Data Platform role where youll 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 onand the latitude to build it the right waythis role is for you.

The Role

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. Youll collaborate directly with product and engineering moving fluidly from specs to query plans to production systems.

What Youll Do

• 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 featuresfast and with high quality
• Establish the data engineering foundation the team will need as the company scales (tooling standards performance practices observability)

What Our Client Is Looking For

Required

• 5 years of hands-on engineering experience with clear evidence youve 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. ClickHouse 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

Nice to Have

• 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

Guiding Principles (Culture Fit)

• Extreme Ownership
• Quality
• Curiosity and Play
• Make Our Customers Heroes
• Respectful Candor

Benefits

• 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

Required Experience:

Staff IC