Data Platform Engineer (Customer-Facing Analytics)Location: (SF / NYC / Remote - confirm preference)
Compensation: $220,000 - $250,000 + equity
Tech Stack: Python, Postgres, Clickhouse, AWS, Kafka, Spark, Airflow.
The RoleOur 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.
What You'll 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 features-fast 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 ForRequired
- 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., 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
