Senior Data Engineer
Ford Motor Company (company site, opens in a new tab)3+ yrsChennaiHybridDirect career pageEst. 20–35 LPA
Posted 20h ago
Reach people at Ford Motor Company
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Ford Motor Company you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead design and implementation of core data capabilities including ETL/ELT pipelines, multi-tier data models, data quality strategies, and analytics infrastructure primarily on Google Cloud Platform (BigQuery).
- Develop and optimize scalable, fault-tolerant data pipelines from diverse batch and streaming sources to support business intelligence and analytics teams across multiple Ford Pro business verticals.
- Collaborate cross-functionally with data scientists, architects, and other stakeholders to ensure timely availability of accurate data and develop shared data services and APIs for internal and external partners.
Minimum Requirements
- Bachelor's degree mandatory.
- Minimum 3 years of experience with SQL and Python programming.
- At least 2 years of experience with GCP or AWS cloud services; strong candidates with 5+ years in traditional data warehouse environments using ETL tools like Informatica also considered.
- 3+ years experience building distributed, fault-tolerant data pipelines; familiarity with relational and non-relational databases required.
Ideal Candidate Profile
- Experienced in designing and operating scalable data engineering solutions in cloud platforms, especially GCP (BigQuery, Dataflow, Cloud Composer).
- Demonstrated ability to manage complex, cross-functional data projects involving both cloud-native and traditional data warehouse environments.
- Comfortable working with a diversity of data sources and technologies including Hadoop, Spark, Kafka, and building APIs to expose data services.