Data Engineer

Little more about the team:

We’re growing our Data team, and this role is a foundational part of that growth. You’ll be our second Data Engineer, focused on expanding our data modeling layer, building trustworthy foundations for analytics, and enabling teams across the company—from Product to GTM to Finance—with scalable, reliable data. This role will report directly to the Head of Data and you’ll partner with them and our Staff Data Engineer to build a reliable data foundation upon which Honeycomb can scale our business.

Why You Might Be Excited

• You’ll help build the data foundation for a high-velocity startup with strong product-market fit and cross-functional data demand
• You’ll be joining early, shaping the standards and structure for our future data practice
• You’ll work closely with a technical Head of Data and Staff Data Engineer—not abstract stakeholders—and have clear lines of ownership
• You’ll have the opportunity to explore generative AI, LLMs, and agent workflows where they make sense (e.g., schema mapping, anomaly detection, code validation)
• You’ll ship things that matter, fast—but thoughtfully

What you’ll do in the role:

• Own and expand our dbt modeling layer, ensuring it is structured, documented, and reliable
• Ingest and normalize data from diverse systems (e.g. Stripe, Salesforce, Hubspot, Netsuite) via Fivetran and custom pipelines
• Collaborate with stakeholders across the business to understand how data is used and what it needs to represent
• Build analysis-ready marts that power dashboards, experimentation, and planning
• Partner with our Staff Data Engineer on ingestion architecture and orchestration, while taking the lead on modeling and semantic logic
• Ensure data quality via testing, validation frameworks, and proactively identifying upstream issues
• Create leverage through documentation, code reusability, and adoption of AI-assisted tooling to boost development velocity, reduce manual overhead, and enhance documentation/testing

What you’ll bring to the role:

• 3–5+ years of experience in data engineering or analytics engineering
• Mastery of SQL and dbt, with an eye for structure, testing, and maintainability
• Working experience with Python, especially for data ingestion, orchestration, and basic scripting
• Comfort working with cloud data warehouses like Snowflake (or Redshift, BigQuery)
• Comfort working through ambiguous situations - our data team is small and young so some role shaping and willingness to work across the data stack (e.g. occasional dashboard building, analysis) is critical to early success in this role
• Experience implementing structured data models, architectures and marts
• Strong intuition around data contracts, late-arriving data, slowly changing dimensions, and business logic edge cases
• Strong intuition around data quality and governance including experience developing tests and building out documentation
• Experience working across functions (e.g., Finance, Product, CS) to map business needs into durable models
• Hands-on usage of AI tooling in your data engineering workflow (e.g., Copilot for dbt model stubs, AI-assisted test generation, documentation, SQL generation/refactoring, or code reviews)—you don’t just use it, you use it thoughtfully to create leverage
• A mindset of ownership, iteration, and clarity—you care about the business logic behind the data, and building systems others can trust and build on