Data Engineer

- You will design, build, and maintain robust, scalable data pipelines and ingestion workflows across a growing Data Lake;

- Define and enforce data quality standards, SLOs, and validation frameworks to ensure accuracy and reliability of critical data assets;

- Continuously optimize existing pipelines for performance and cost efficiency as data volumes scale;

- Expand and own our monitoring and alerting coverage - surfacing data issues before they become customer-facing problems;

- Drive best practices around data modeling, partitioning, and compute resource utilization;

- Also, you get to drive 100,000 lb excavators.

- 5+ years of experience in data engineering, with a strong track record in large-scale data lake or data warehouse environments

- 5+ years of experience working with SQL and distributed query engines (e.g. Spark, BigQuery, Snowflake, or similar)

- Deep proficiency with pipeline orchestration tools (e.g. Airflow, Prefect, or equivalent) and transformation frameworks (e.g. Spark)

- Experience designing and implementing data quality frameworks - validation, anomaly detection, lineage tracking

- Familiarity with observability tooling for data systems: monitoring, alerting, and incident response for data pipelines

- Experience enabling non-engineering stakeholders to self-serve on data infrastructure, whether through documentation, tooling, or hands-on enablement

- Hands-on experience with Databricks and Spark

- Experience with streaming or near-real-time ingestion patterns

- Familiarity with data governance and access control at scale

- Background working on customer-facing data products or external SLAs

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.