Data Engineering Leadership & Management: 

Proven experience (10+ years) in a Data leadership role managing engineering teams, ideally with a focus on Datalake , Lakehouse platform engineering, DataOps/AIOps in GCP/Azure/AWS, or similar areas.  Experience leading and mentoring engineering teams, fostering a culture of innovation, continuous learning, and technical excellence. Demonstrated ability to drive strategic technical decisions and ensure alignment with broader organizational goals.  Proven ability to build and maintain high-performing teams, promoting accountability, ownership, and collaboration. Experience with performance management, including conducting performance reviews and providing constructive feedback.  Excellent communication and interpersonal skills, with a proven ability to cultivate cross-functional collaboration and build strong relationships with stakeholders at all levels. 

II. Agile & Scrum Practices: 

Deep understanding and practical experience with Agile methodologies (Scrum, Kanban), including facilitating daily stand-ups, sprint planning, backlog grooming, and sprint retrospectives.  Experience working closely with Product Managers, Purchase Services, System Integrators to align engineering efforts with product goals, ensure well-defined user stories, and manage priorities effectively.  Proven ability to ensure engineering rigor in story hygiene, including clear acceptance criteria, well-defined dependencies, and a focus on deliverability within the sprint. 

III. Technical Expertise & Accountability: 

Deep understanding of Data engineering principles in Log Analytics and experience in designing, building, and maintaining scalable and reliable Data Pipelines in GCP /Azure.  Expertise in DevOps practices, including CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), infrastructure-as-code (Terraform, Ansible, CloudFormation), and automation.  Proficiency in at least one programming language (e.g., Python, Java) sufficient to effectively communicate with and guide your engineering team.  Strong understanding of cloud solutions and offerings (preferably GCP services – Dataflow, Pub/Sub, Cloud Logging, Compute Engine, Kubernetes Engine, Cloud Functions, Big Query,  Cloud Storage, Vertex AI). Experience with other major cloud providers (AWS, Azure) is also valuable.  Experience with designing and implementing microservices and serverless architectures. Experience with containerization (Docker, Kubernetes) is highly beneficial.  Experience with monitoring and optimizing platform performance, ensuring systems are running efficiently and meeting SLAs. Proven ability to lead incident management efforts and implement continuous improvements to enhance reliability.  Commitment to best engineering practices, including code reviews, testing, and documentation. A focus on building maintainable and scalable systems is essential. 

IV. Operational Excellence & Cost Optimization: 

Proven ability to drive cost optimization initiatives, particularly in cloud infrastructure and resource usage, aligning with Ford’s broader cost-reduction goals.  Experience tracking and reporting key metrics for your domain/platform related to team performance, including quality and operational efficiency.  10+yrs of individual contributor experience in building efficient data pipelines at enterprise scale. B.E/BTech or MCA or equivalent degree.  GCP/AZURE/AWS certification.  Execute Data Operations for Enterprise foundational services team.  Lead a lean team of Data Engineers, Data Scientists, Cloud and Platform Engineers. Develop data engineering pipelines, frameworks focused on modularity & Data craftsmanship. Execute Data Engineering projects for IT Operations focused on Log data management & analytics. Democratize “Data as a service" for all interfacing systems and data consumers. Execute strategies for data management, data governance and data quality across the enterprise IT operations domain. Serve as role model for platform/technology experts with strong emphasis on bias towards action.