Continental - IT Global Team Lead - Data Engineering

About the job :

- Lead and manage the global IT team for data engineering that develops all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation.

- This team combines both lakehouse modeling of common and business use ase artefacts & semantics, as well as generalist data integration & metadata services.

- Ensure high-quality delivery of data engineering assets that enable business analytics, AI/ML integration, and data governance at scale.

- Act as the delivery and people manager for the data engineering team co-located in Bengaluru, collaborating globally with platform, business, and other IT stakeholders.

- Drive consistency, engineering excellence, and cross-domain reuse across the entire data engineering lifecyclefrom data acquisition to semantic layer delivery, while applying rigorous software engineering practices in data engineering such as modular design, test-driven development, and artifact reuse in all implementations

- Direct management of approx.

- 1015 data engineers (generalists and specialists).

- Reports to the global head of Data & Analytics within the IT Competence Center.

- Team delivers data engineering & analytics assets via Product Owner for data & analytics to all business domains.

- Collaborates with Product Owners, Lead Architects & Lead engineers, Data Governance, Infrastructure & Cybersecurity, and domain-aligned functional IT teams globally.

Main Tasks:

- Line management for a high-performing, cross-functional data engineering team.

- Drive skill development, mentorship, and performance management.

- Foster a culture of accountability and trust.

- Own timely delivery of data & analytics assets from data acquisition to semantic layers.

- Align work with business priorities and architectural standards.

- Ensure quality gates and documentation.

- Act as primary escalation and coordination point across business domains.

- Bridge infrastructure, functional IT, cybersecurity, and platform decisions.

- Advocate for team in global forums.

- Guide adoption of engineering best practices (TDD, CI/CD, IaC) & guide building all technical artefacts as code, creating scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala

- Leading the design and operation of scalable batch/stream pipelines in Databricks, including ingestion from structured/semi-structured sources and implementation of bronze/silver/gold layers under lakehouse governance.

- Overseeing dimensional modeling and curated data marts for analytics use cases, while ensuring semantic layer compatibility and collaboration on enterprise 3NF warehouse integration.

- Ensuring high-quality engineering practices across data validation, CI/CD-enabled TDD, performance tuning, metadata governance, and stakeholder collaboration via agile methods.

- Build an inclusive, high-performance team culture in Bengaluru.

- Champion DevSecOps, reuse, automation, and reliability.

- Commit all artifacts to version control with peer review and CI/CD integration

- Ensure documentation, knowledge sharing, and continuous improvement.

- Leading the design and operation of scalable, secure ingestion servicesincluding CDC, delta, full-load, and SAP extractions via tools like Theobald Extract Universal.

- Overseeing integration with APIs, legacy systems, Salesforce, and file-based sources, while aligning all interfaces with cybersecurity standards and compliance protocols.

- Driving the development of the enterprise data catalog application, supporting dataset discoverability, metadata quality, and Unity Catalogaligned access workflows.

Qualifications :

- Degree in Computer Science, Data Engineering, Information Systems, or related discipline.

- Certifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant DevOps certifications).

- Minimum 8 years in enterprise data engineering, including data ingestion and pipeline design.

- Experience across structured and semi-structured source systems is required.

- Demonstrated experience building production-grade codebases in IDEs, with test coverage and version control.

- Hands-on experience with secure SAP/API ingestion, lakehouse development in Databricks, and metadata-driven data platforms.

- Delivered high-impact enterprise data products in cross-functional environments.

- At least 3 years of team leadership or technical lead experience, including hiring, mentoring, and representing team interests in enterprise-wide planning forums.

- Demonstrated success leading globally distributed teams and collaborating with stakeholders across multiple time zones and cultures