Responsibilities & Key Deliverables

Key Tasks and Deliverables:

• Define and implement a scalable, unified data platform architecture for real-time automotive data ingestion, processing, and storage aligned with organizational goals.
• Oversee end-to-end data integration from vehicle telemetry, IoT sensors, customer feedback, and service records into a centralized repository.
• Ensure high data quality through rigorous validation, cleansing, versioning, and governance processes to maintain a single source of truth.
• Establish and enforce data security and privacy compliance, managing controlled access and audit trails.
• Standardize data engineering tools, coding practices, and automation to enhance platform efficiency and maintainability.
• Lead and develop a high-performing data engineering team, fostering technical expertise in big data, cloud architecture, and IoT data management.
• Collaborate cross-functionally with analytics, system intelligence, and insight delivery teams to translate business requirements into robust data infrastructure solutions.

Critical Must-Have Deliverables:

• A fully operational, scalable data platform capable of handling high-volume, real-time automotive and customer data.
• Robust, automated data pipelines ensuring seamless, resilient data ingestion and integration.
• Comprehensive data governance framework ensuring data quality, security, and compliance.
• A skilled and motivated data engineering team aligned with evolving technology trends and organizational objectives.
• Documented and standardized data engineering practices and tooling across the platform.

Experience

• Minimum 10 years of progressive experience in data engineering or related fields, with at least 5 years in a leadership role managing large-scale data platforms.

Industry Preferred

• Automotive, IoT, Technology, or Data-Driven Product Development industries.

Qualifications

• Bachelor’s or Master’s Engineering in Computer Science, Information Technology, Data Science, or related technical discipline.
• Proven expertise in big data technologies, cloud platforms (e.g., AWS, Azure, GCP), and real-time data processing frameworks.

General Requirements

Good-to-Have Deliverables:

• Implementation of advanced analytics enablement features such as real-time streaming analytics and predictive data models.
• Continuous innovation initiatives introducing emerging technologies and methodologies in data engineering.
• Cross-functional training programs to enhance data literacy and collaboration across business units.
• Contributions to industry forums or publications showcasing the organization’s data engineering capabilities.

Competencies and Skills:

Functional Competencies:

• Expertise in data platform architecture, data lake/warehouse design, and pipeline development.
• Strong knowledge of data integration techniques, ETL/ELT processes, and real-time streaming data.
• Proficiency in data governance, quality assurance, and security best practices. Tools and Techniques Used:
• Advanced proficiency with big data tools (e.g., Apache Spark, Kafka), cloud services (AWS/Azure/GCP), and data orchestration frameworks.
• Experience with containerization, automation tools, and version control systems.

Behavioral Competencies:

• Strategic thinking with a results-driven mindset.
• Strong leadership and team development capabilities.
• Excellent communication and stakeholder management skills.
• Adaptability and continuous learning orientation.

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