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What You'll Do
- Engineering & Delivery: Independently design, build, and maintain complex, production-grade data pipelines on Databricks.
- Develop efficient ETL/ELT processes with a strong focus on data quality, consistency, and scalability.
- Build reusable frameworks for ingestion, transformation, and reconciliation across enterprise source systems.
- Apply and help improve engineering standards — pipeline architecture, coding standards, and ETL/ELT best practices.
- Technical Mentorship: Mentor junior engineers through code reviews, design reviews, and pair-programming on complex problems.
- Share best practices in Databricks/PySpark, coding standards, and engineering discipline.
- Contribute to a culture of ownership, automation, and continuous improvement.
- Operations & DevOps: Deploy changes through CI/CD and the Change Request (CR) lifecycle, including validation, release management, and ticket closure.
- Participate in problem management and root-cause analysis — driving permanent fixes and automation over recurring firefighting.
- Support the operational health of business-critical data workloads — monitoring, alerting, and incident response.
- Collaboration: Partner with Reporting, Visualization, Platform, and Business teams to expose curated datasets for downstream analytics consumers.
- Communicate technical trade-offs, progress, and risks clearly to technical and non-technical stakeholders across geographies.
- Document workflows, standards, and runbooks to ensure reproducibility and knowledge continuity.
- What Success Looks Like (First 6–12 Months): In your first 6–12 months, you'll independently deliver key data pipelines to a high standard, strengthen data quality and CI/CD practices in your area, reduce recurring incidents through problem management, and become a go-to technical resource for the team.
- Required Qualifications: Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience. 6+ years of experience in data engineering.
- Strong hands-on background in Databricks, Python (PySpark), and SQL for large-scale data processing.
- Proven experience designing and delivering production data pipelines (ETL/ELT) at enterprise scale.
- Working knowledge of CI/CD pipelines, Git-based branching strategies, and DevOps practices.
- Experience with cloud platforms (AWS preferred) and core data services.
- Experience supporting production data pipelines, including monitoring, alerting, and incident response.
- Strong communication skills across engineering and business audiences.
Nice to Have
- Experience with orchestration frameworks and streaming technologies.
- Exposure to Infrastructure-as-Code and modern deployment tooling.
- Familiarity with observability tooling for data platforms.
- Background in semiconductor manufacturing or large-scale industrial data processing.
- Databricks Certified Data Engineer Associate or Professional certification is a plus.
- Competencies: Ownership and accountability — end-to-end responsibility for your pipelines, from design to production support.
- Problem-solving orientation — bias toward permanent fixes and automation.
- Technical depth — leads by example through hands-on engineering and high standards.
- Collaboration — works well with Reporting, Platform, and Business teams across geographies.
- Clear communication — articulates technical trade-offs to non-technical stakeholders.
- More information about NXP in India... #LI-7013
Sourced directly from NXP Semiconductors’s career page
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Specialisation
Open roles at NXP Semiconductors
779 positions
Job ID
/job/Bangalore/Lead-Data-Engineer_R-10066224
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