Data Engineer,DIgital Analytics and Experimentation
Ford Motor Company (company site, opens in a new tab)5+ yrsChennaiOn-siteDirect career pageEst. 14–26 LPA
Posted 1d ago
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Job description
Structured overview of role & requirements
About This Role
- Design, build, and maintain scalable data pipelines and curated data assets for digital analytics, KPI reporting, experimentation, AI-enabled insights, and enterprise digital products.
- Translate analytical and product requirements into reliable data models, automated validation frameworks, and documented technical assets supporting Ford’s global digital ecosystem.
- Implement automated data quality checks, anomaly detection, and monitoring to enhance data trustworthiness and reduce manual effort, while collaborating globally to align with common KPI definitions and reporting standards.
Minimum Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, Information Systems, Engineering, Statistics, Mathematics, or related field, or equivalent experience.
- 5+ years of experience in data engineering, analytics engineering, business intelligence engineering, digital analytics engineering, or related technical data role.
- 3+ years hands-on experience using SQL to build, transform, join, validate, and optimize large-scale datasets.
- Experience building and maintaining data pipelines, data models, ETL/ELT workflows, APIs, orchestration jobs, or production analytics datasets in cloud or enterprise environments; familiarity with Python, PySpark, dbt, Airflow, or comparable tools.
Ideal Candidate Profile
- Deep technical fluency in data engineering combined with strong digital analytics context and practical AI tool application experience to improve analytics delivery speed and quality.
- Experience working in global, cross-functional teams including analytics leads, product managers, tagging and engineering teams to ensure data products meet measurement and reporting needs.
- Proven ability to document data lineage, technical assumptions, validation rules, and data quality considerations clearly for diverse stakeholders and to use modern engineering practices like version control, code reviews, CI/CD, and release governance.