Data Engineering Intern at Efficy supporting data management and ETL pipeline development. Collaborate with teams and contribute to the enhancement of data architecture.
About the role
- Senior Data Engineer developing data pipelines and infrastructure on Google Cloud Platform for WorkWhile's staffing marketplace. Collaborating with Data Science and Engineering teams to enhance data quality and availability.
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.
Benefits
- Remote-friendly work culture with office hubs in SF, NY, Seattle & Toronto
- In-person company offsites
- Medical, dental, & vision coverage
- Flexible time off
- 401(k) with employer match
- WFH stipend to support your home office needs
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