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Senior data-engineer title, metro hiring, and broad cloud/stack requirements increase applicant competition.
Core data engineering skills transfer, but identity-graph and AdTech preferences moderately limit cross-industry fit.
Explicit 8+ years and deep data platform, cloud, and production experience make filters highly strict.
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Design, build, and optimize scalable data pipelines and ETL/ELT workflows for large, complex datasets processing billions of records.
Own end-to-end development and optimization of identity resolution and ID graph systems, including data ingestion, normalization, matching, and deduplication.
Make and influence technical decisions on data architecture, scalability, and system design while ensuring data quality and performance.
8-12+ years of hands-on experience in data engineering or large-scale data processing.
Proven experience building production-grade data pipelines and distributed systems handling hundreds of millions to billions of records.
Strong expertise in SQL and Python for large-scale data processing and analysis.
Experience with Google Cloud Platform (BigQuery, Dataflow, Pub/Sub) and/or AWS (S3, Redshift, EMR).
Experienced with identity graphs, entity resolution, or record linkage systems, preferably in AdTech, digital identity, or audience data platforms.
Demonstrated ability to drive complex system design and deliver scalable, production-ready data solutions independently in a fast-paced, remote environment.
Strong analytical, debugging, and data-driven mindset with prior exposure to data quality monitoring and modern data platforms (Snowflake, Databricks).