About the role
- We’re looking for a Lead Data/Analytics Engineer to own how Dutch measures its product and business
- You’ll run the event instrumentation pipeline, build the models and metrics layers that teams use to make decisions, and sit shoulder-to-shoulder with product managers to design and analyze experiments
- Ts. When data engineering work needs doing, you can pick it up without missing a beat
- This is a senior individual contributor role. You’ll work across our modern data stack: Segment and Amplitude for behavioral data, Snowflake and dbt for warehousing and modeling, Prefect for orchestration, and Sigma for BI
- You’ll partner with the BI team (which governs all data model builds) and with product engineering to make sure every dashboard, metric, and experiment runs on data the company trusts
- Product Analytics & Experimentation (40%)
- Own the event pipeline from instrumentation through Segment into Amplitude, Iterable, and Snowflake, including sources, destinations, and reverse ETL
- Define and publish a typed event schema and governance workflow that frontend teams build against, so event data stays consistent across web and mobile
- Design and validate tracking plans for new features and surfaces, and confirm events flow end to end before launch
- Partner with product managers to design experiments in Amplitude: define hypotheses, select metrics, set guardrails, and size tests
- Build Amplitude charts, funnels, cohorts, and dashboards that help product teams answer their own questions without waiting on you
- Debug identity resolution, user properties, and experiment assignment issues across Segment, Amplitude, and downstream tools
- Analyze experiment results and present findings to stakeholders with clear recommendations
- Data Engineering & Infrastructure (35%)
- Build, maintain, and optimize ELT pipelines using dbt, Prefect and Fivetran
- Own core datasets and dbt models in Snowflake in partnership with the BI and product engineering teams
- Lead infrastructure modernization: orchestration and dbt version upgrades, flow runtime reduction, Snowflake compute and storage cost optimization
- Drive warehouse security and governance work, including access controls, remediation of security assessment findings, and PII handling
- Implement data quality tests, freshness checks, observability, and alerting across critical pipelines
- Troubleshoot pipeline failures and data incidents, and drive root-cause fixes rather than patches
- Metrics, Semantic Layer & AI Data Products (25%)
- Deliver ready-to-consume data marts that power product features such as recommendations and personalization, plus the baselines to measure them
- Contribute to the semantic layer that gives BI tools and AI features a single source of truth for metric definitions
- Supply reliable, well-governed data to AI features that improve the member experience and vet workflows
- Define and document the company’s core metrics so every team calculates things the same way- Experience with orchestration tools such as Prefect, Airflow, or Dagster
- Uses AI tools (code assistants, LLMs, etc.) as part of daily work and can show how
- Familiarity with BI tools like Sigma, Looker, or Tableau
- Comfortable with Git-based workflows and CI/CD tools such as GitHub Actions
- Experience with product analytics platforms, ideally Amplitude, including funnels, cohorts, experiment configuration, and identity resolution
- Strong proficiency in SQL and Python
- 6+ years of experience in analytics engineering, data engineering, or hybrid data roles
- Experience with customer data platforms and event pipelines, ideally Segment, including tracking plan design, destination management, and debugging
- Bonus: experience with semantic layers, data contracts, ML/LLM applications in production, or cloud infrastructure (AWS, GCP, or Azure)
- Deep hands-on experience with Snowflake or a comparable cloud data warehouse
- Demonstrated experience designing and analyzing A/B tests or product experiments
- Production experience with dbt, including testing, documentation, and CI
- You’re as comfortable writing dbt models as you are reviewing an experiment readout with a product lead
- You treat trust in the numbers as the product. When a metric is wrong, you feel it personally
- You can build an event taxonomy from scratch and also explain to a PM why their experiment needs a bigger sample size
- You use AI tools every day to move faster and expect to keep pushing that boundary
- You love animals
- You care about data quality, reproducibility, and documentation because you’ve been burned by their absence
- You’re self-directed and comfortable managing your own priorities in a fast-moving environment
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