Context

Collective.work is building the next-generation AI-powered sourcing platform for recruiters. Our mission is to help talent teams identify, engage, and hire the best candidates faster through intelligent automation and data-driven insights. We operate at the intersection of data, AI, and recruiting workflows—where high-quality data infrastructure is critical to our success.

Missions

• Design and maintain scalable data pipelines (batch and real-time)

• Build and optimize ETL/ELT workflows across Azure and/or GCP

• Develop data models and architectures to support analytics and ML use cases

• Ensure data quality, integrity, and reliability across systems

• Collaborate with ML engineers to prepare and serve training datasets

• Monitor and improve pipeline performance, cost efficiency, and scalability

• Implement best practices for data governance, security, and compliance

• Contribute to tooling and infrastructure decisions

Tools & Environment

• Cloud: Azure (Data Factory, Synapse) and/or GCP (BigQuery, Dataflow)

• Data Processing: Python, SQL, Spark

• Orchestration: Airflow / Prefect

• Storage: Data lakes, warehouses

• Streaming: Kafka / PubSub (nice to have)

• DevOps: Docker, CI/CD

Working Conditions

• Flexible remote work environment

• Opportunity to work on a product at the cutting edge of AI and recruiting

• High ownership and impact from day one

• Collaborative, product-driven engineering culture

• Opportunity to shape the data foundation of a growing platform