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Senior Data Engineer, Engineering

Builds and optimizes data pipelines and BigQuery warehouse on GCP to power analytics, ML models, and operational intelligence for staffing marketplace. Partners with Data Science and Engineering on ML workflows and infrastructure.

175k – 195kSan Francisco, CANew York, NYSeattle, WAData EngineeringHybrid5+ YOE
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About the role

Responsibilities

  • 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

Requirements

  • 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

Nice to Have

  • Experience with reverse ETL tools like Hightouch or Census
  • Familiarity with BI tools such as Metabase or Looker
  • Background in marketplace, staffing, or gig economy data domains

Skills

SQLPythonBigQueryDagsterGCPPostgresTerraformKubernetesDockerAirflow

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