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. Python (PySpark) . SQL . Data Pipelines . 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.
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.
Operations & DevOps:
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.
Communicate technical trade-offs, progress, and risks clearly to technical and non-technical stakeholders across geographies.
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.

Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience.

- 6+ years of experience in data engineering.
- Solid 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.
- 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.

Problem-solving orientation - bias toward permanent fixes and automation.
Clear communication - articulates technical trade-offs to non-technical stakeholders.

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📌 Lead AWS Data Engineer (Permanent) (Bengaluru)
🏢 NXP Semiconductors India Private
📍 Bengaluru