Job description
We're hiring a Data Engineer to build the data infrastructure that powers analytics, ML models, and business intelligence across the company. You'll work with large-scale data systems, designing pipelines that are reliable, observable, and easy to maintain. This is a high-leverage role at the intersection of engineering and data science, with significant impact on how the business makes decisions.
What you'll do
- Design, build, and maintain batch and streaming data pipelines using tools like Airflow, dbt, Kafka, and Spark.
- Model and transform raw operational data into curated, analytics-ready datasets used by BI dashboards and ML feature stores.
- Implement data quality checks, lineage tracking, and alerting to ensure data consumers can trust the data they rely on.
- Collaborate with data scientists and ML engineers to build scalable feature engineering pipelines for production models.
- Partner with engineering teams to define event schemas and ensure applications emit the right instrumentation for downstream use.
What we're looking for
- At least 3 years of professional data engineering experience building production-grade pipelines at scale.
- Strong SQL skills and experience with data warehouses such as Snowflake, BigQuery, Redshift, or ClickHouse.
- Hands-on experience with workflow orchestration tools (Airflow, Prefect, or Dagster) and transformation frameworks like dbt.
- Proficiency in Python for data processing, scripting, and ETL automation.
- Understanding of streaming architectures (Kafka, Kinesis) and the trade-offs between batch and real-time approaches.
We offer competitive compensation with equity, full health benefits, unlimited PTO, $3,000 annual learning budget for conferences and courses, and direct access to leadership. Data engineers are first-class citizens here — we invest in modern tooling and give the team the autonomy to drive architectural decisions.