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Tier-1 brand, mid-level generalist data role, and metro location drive high applicant competition.
Core data engineering skills are broadly transferable across industries, so background fit sensitivity is low.
Explicit years, mandatory data pipeline skills and cloud/ETL tech make shortlisting highly strict.
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Lead design and development of ETL/ELT data pipelines to ingest and process data into GCP BigQuery for business intelligence and analytics.
Build cloud-native services and APIs for data-driven solutions, collaborating closely with data scientists and multiple internal Ford organizations.
Manage and optimize scalable, fault-tolerant data pipelines; provide strategic advice on data transformation and usage across the enterprise.
Bachelor's degree required.
Minimum 3 years of experience with SQL and Python.
Minimum 2 years experience with GCP or AWS cloud services; or 5+ years in traditional data warehouse ETL with Informatica.
Minimum 3 years experience building data pipelines in distributed and fault-tolerant environments.
Experienced in both cloud data platforms (preferably GCP) and traditional data warehouse environments, capable of bridging the two.
Strong technical expertise in building and managing scalable, reliable data pipelines and APIs for enterprise use.
Comfortable working cross-functionally with analytics, data science, and business teams within a large enterprise, often in ambiguous environments.