Staff Data Engineer

Role overview

Own the data platform that supports company reporting and analytics, spanning ingestion pipelines, the warehouse, data models, and observability. This is a senior individual contributor role with broad ownership: make key metrics trustworthy, establish reliable operating practices, and deliver foundational work that helps business teams understand performance.

Responsibilities - Take ownership of warehouse models and orchestration, including dbt transformations and production pipeline operations. - Improve lineage, alerting, documentation, and data quality so stakeholders can trace and trust priority metrics. - Work with Finance on reporting logic such as cash and accrual views, incentives, and revenue definitions. - Coordinate with Engineering on production schema changes and movement of operational data into the warehouse. - Deliver a substantial early project, such as adding accrual reporting or integrating historical practice data with existing performance metrics. - Establish repeatable processes for change review, failure triage, security checks, and infrastructure cost and performance tracking.

Requirements - Five or more years of data engineering experience, including time building or maturing a business-critical data system. - Deep SQL and dbt modeling experience, with the ability to reason about financial logic and downstream metric impacts. - Python and software engineering fundamentals, plus production orchestration experience with Prefect, Airflow, Dagster, or similar. - Experience handling schema evolution from operational databases into a warehouse, with effective alerting and observability. - Track record of leading work end to end with non-technical partners, from requirements and architecture through delivery. - Ownership of reliability and cost, including a willingness to establish SLAs, runbooks, operating metrics, and practical safeguards for AI-assisted data products.

Nice to have - Familiarity with Snowflake, Airbyte, Omni, GitHub, and AI coding or workflow tools. - A considered perspective on where language models can help in a data stack and where controls are needed.

Benefits and work setup - Small team with substantial input into data conventions, architecture, and roadmap. - Close collaboration with Product and Engineering, using a modern warehouse and analytics stack.