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Mid-level, popular data-engineer role in a metro with broad GCP/BigQuery requirements increases competition.
Medium — core data engineering skills transferable, but identity-resolution and GCP specifics add domain bias.
Explicit 5–8 years plus mandatory GCP, BigQuery, Python, and large-scale data experience makes shortlisting strict.
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Design, build, and optimize scalable data pipelines and ETL/ELT workflows for large, complex datasets, including identity resolution and ID graph systems.
Take end-to-end ownership of complex data problems from design through production deployment and collaborate with cross-functional teams to deliver production-ready data solutions.
Contribute to key technical decisions on data architecture, scalability, system design, and continuously improve system performance, data quality, and pipeline resilience.
5–8+ years of hands-on experience in data engineering or large-scale data processing.
Strong expertise in SQL (Postgres, BigQuery, Redshift), Python, and Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Functions) and/or AWS (S3, Redshift, EMR, RDS).
Experience with distributed data processing, data modeling, query optimization, and handling datasets of hundreds of millions to billions of records.
Familiarity with CI/CD, containerization (Docker, Kubernetes), version control (Git), and Agile tools.
Experienced in architecting and delivering large-scale, mission-critical data platforms with production-grade pipelines.
Strong contributor to system design and independent implementation with ability to troubleshoot and optimize complex data workflows.
Comfortable working remotely in a fast-paced, collaborative environment with a data-driven and analytical mindset.