Senior Data Engineer, Engineering

About WorkWhile WorkWhile is on a mission to help workers earn a better living and live better lives. More than 80 million Americans work hourly jobs, often with unpredictable schedules that pay close to minimum wage and offer limited benefits, leading to financial instability. WorkWhile is revolutionizing frontline work by creating a more flexible and efficient labor market. Powered by AI, the WorkWhile platform helps businesses quickly access high-quality talent. Workers benefit from same-day pay, free telehealth services, upskilling, and the ability to choose shifts that match their skills and schedules. We have the audacious goal to align incentives and solve inefficiencies in the labor market, disrupting the $650 billion staffing and recruiting market. Backed by Khosla Ventures and recognized by the Inc. 5000 for 10x growth, WorkWhile is reshaping the future of work. About the role WorkWhile is seeking a Senior Data Engineer to join the Engineering team and lead the development and operation of our data platform powering analytics, ML models, and operational intelligence across the WorkWhile staffing marketplace. In this role, you will be responsible for designing, building, and maintaining data pipelines and infrastructure on Google Cloud Platform that enable our Data Science team to train and deploy models at scale - from demand forecasting and worker reliability prediction to dynamic pricing and retention modelling. You will own and evolve our Dagster-based orchestration platform, BigQuery data warehouse, and the integrations that connect our PostgreSQL production database to our analytical layer. You will work closely with Data Science, Product Engineering, and Operations to ensure reliable, well-modelled, high-quality data is available where and when it’s needed. Your work will directly improve how WorkWhile matches workers with opportunities, predicts marketplace dynamics, and delivers a better experience for workers and businesses alike. Expect to work in a fast-paced, collaborative environment where your contributions directly support WorkWhile’s mission to make hourly work more accessible, reliable, and rewarding for everyone. What you'll do - 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 What we're looking for - 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 e