Lead Data Engineer

Job Description **Position Summary:** --------------------- We are looking for a hands-on Senior Data Engineer with a strong DevOps mindset to design, build, and operate reliable, scalable, and observable data pipelines that power business functions across the enterprise. This is a senior individual-contributor role — you'll independently own the delivery of complex pipelines, uphold engineering standards, deploy via CI/CD, support the operational health of the platform, and mentor junior engineers through reviews and collaboration. **Core Skills:** ---------------- Databricks · Python (PySpark) · SQL · Data Pipelines · CI/CD **Key Responsibilities:** ------------------------- ### **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. **Preferred Qualifications:** ----------------------------- * 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...](https://www.nxp.com/company/about-nxp/worldwide-locations/india:INDIA) #LI-7013