Principle Engineer

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, Pub/Sub, 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.

• 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 Personas.
• As a Principal Engineer ensure the data pipelines built by teams are robust, scalable and meets the highest standards.
• Execute strategies for data management, data governance and data quality across the enterprise IT operations domain.
• Serve as role model for data engineering experts with strong emphasis on bias towards action.