Build and optimize data pipelines that ingest, transform, and model data from PostgreSQL, Amplitude, and external sources into BigQuery
Own BigQuery data warehouse architecture: dataset organization, table design, partitioning, clustering, and query performance optimization
Work to improve Ops ML platform capabilities and processes, partnering with the Data Science team to support efficient and reliable ML training and pipelines
Work on reverse ETL workflows and API integrations that push model predictions back into production systems
Support analytics by ensuring clean, performant datasets are available for self-serve reporting
Collaborate with Engineering on Terraform-managed GCP infrastructure
Optimize Cloud Tasks and Cloud Scheduler configurations for data refresh jobs and materialized view maintenance

Bachelor’s degree in Computer Science, Data Engineering, Mathematics, or equivalent experience
5+ years in data engineering or data platform engineering
Experience with Dagster or similar orchestration tools (Airflow, Prefect)
Expertise in SQL, with the ability to write and optimize complex analytical queries across BigQuery and PostgreSQL
Proficiency building data pipelines in Python
Experience maintaining data warehouses on BigQuery, Snowflake, or Redshift
Hands-on experience with Google Cloud Platform services
Familiarity with ML workflows and the ability to collaborate with Data Scientists on feature engineering, training pipelines, and model serving
Experience with infrastructure-as-code (Terraform) and containerized deployments (Docker, ECS, Cloud Run, Kubernetes, etc.)
Proficiency with data quality frameworks, monitoring, and observability tooling
Strong collaboration skills and a track record of partnering effectively with Data Science and Product Engineering teams
Passion for building reliable, well-tested data systems
you care about code quality (linting, type checking, CI) as much as pipeline uptime.

Senior Data Engineer

Key skills