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Remote-friendly, popular mid-level data engineer role at a known global firm increases applicant competition.
Transferable core data engineering skills but identity-graph/AdTech experience increases specificity.
Explicit 5–8+ years plus mandatory GCP/BigQuery, Python and large-scale pipeline experience.
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Design, build, and optimize large-scale, scalable data pipelines and ETL/ELT workflows that handle billions of records from diverse sources.
Develop and maintain foundational data architecture and systems supporting identity resolution and ID graph construction, including ingestion, normalization, matching, and deduplication.
Take end-to-end ownership of complex data problems, making key technical decisions and delivering production-ready, high-performance data solutions using Python, SQL, and Google Cloud Platform services.
5–8+ years of hands-on data engineering or large-scale data processing experience.
Proven expertise in SQL (Postgres, BigQuery, Redshift) and Python for large-scale data processing.
Experience with Google Cloud Platform products (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Functions) and/or AWS services (S3, Redshift, EMR, RDS).
Work Experience Required: 5–8+ years explicit in JD. Notice period: Not explicitly mentioned in the JD.
Experienced in contributing to the architecture and delivery of mission-critical, high-scale data platforms and distributed systems.
Comfortable operating independently in a remote, fast-paced environment and able to quickly understand and improve existing complex data systems.
Able to translate complex business requirements into performant, cost-effective data solutions with a strong data-driven and analytical approach.