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
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Design and maintain scalable data pipelines (batch and real-time)
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Build and optimize ETL/ELT workflows across Azure and/or GCP
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Develop data models and architectures to support analytics and ML use cases
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Ensure data quality, integrity, and reliability across systems
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Collaborate with ML engineers to prepare and serve training datasets
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Monitor and improve pipeline performance, cost efficiency, and scalability
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Implement best practices for data governance, security, and compliance
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Contribute to tooling and infrastructure decisions
Tools & Environment
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Cloud: Azure (Data Factory, Synapse) and/or GCP (BigQuery, Dataflow)
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Data Processing: Python, SQL, Spark
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Orchestration: Airflow / Prefect
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Storage: Data lakes, warehouses
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Streaming: Kafka / PubSub (nice to have)
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DevOps: Docker, CI/CD
Working Conditions
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Flexible remote work environment
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Opportunity to work on a product at the cutting edge of AI and recruiting
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High ownership and impact from day one
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Collaborative, product-driven engineering culture
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Opportunity to shape the data foundation of a growing platform