Senior Data Engineer

NXP Semiconductors; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

NXP Semiconductors in Bengaluru is seeking a hands-on Data Engineer to design, build, and operate scalable data pipelines powering enterprise functions. You’ll deploy via CI/CD, maintain data quality, and grow toward broader ownership with guidance from senior engineers.

The role requires 4+ years of data engineering experience with Databricks, Python (PySpark), and SQL, plus familiarity with AWS and production monitoring.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience.
• 4+ years of experience in data engineering.
• Hands-on experience with Databricks, Python (PySpark), and SQL for data processing and transformation.
• Experience designing and delivering production data pipelines (ETL/ELT).
• Working knowledge of CI/CD pipelines and Git-based branching strategies.
• Familiarity with cloud platforms (AWS preferred) and core data services.
• Experience supporting production data pipelines, including monitoring, alerting, and incident response.
• Good communication skills across engineering and business audiences.

Responsibilities

• Design, build, and maintain production-grade data pipelines on Databricks.
• Develop efficient ETL/ELT processes with focus on data quality, consistency, and scalability.
• Contribute to reusable frameworks for ingestion, transformation, and reconciliation across enterprise source systems.
• Apply established engineering standards — pipeline architecture, coding standards, and ETL/ELT best practices.
• Deploy changes through CI/CD and the Change Request lifecycle, including validation and ticket closure.
• Participate in problem management and root-cause analysis, driving permanent fixes and automation.
• Support the operational health of data workloads — monitoring, alerting, and incident response.
• Collaborate with Reporting, Visualization, Platform, and Business teams to deliver curated datasets.

Skills

Databricks

Python (PySpark)

SQL

Data Pipelines

CI/CD

Education

Bachelor's or Master's degree in Computer Science/Information Technology or equivalent

Job description

Position Summary

We are looking for a hands-on Data Engineer with a growing DevOps mindset to help design, build, and operate reliable, scalable data pipelines that power business functions across the enterprise. In this role, you'll build and maintain data pipelines, contribute to engineering standards, deploy via CI/CD, and support the operational health of the platform — working independently on defined tasks while growing toward broader ownership with guidance from senior engineers.

Position Summary

We are looking for a hands-on Data Engineer with a growing DevOps mindset to help design, build, and operate reliable, scalable data pipelines that power business functions across the enterprise. In this role, you'll build and maintain data pipelines, contribute to engineering standards, deploy via CI/CD, and support the operational health of the platform — working independently on defined tasks while growing toward broader ownership with guidance from senior engineers.

Core Skills

Databricks

• Python (PySpark)
• SQL
• Data Pipelines
• CI/CD

Key Responsibilities

Engineering & Delivery:

• Design, build, and maintain production-grade data pipelines on Databricks.
• Develop efficient ETL/ELT processes with a strong focus on data quality, consistency, and scalability.
• Contribute to reusable frameworks for ingestion, transformation, and reconciliation across enterprise source systems.
• Apply established engineering standards — pipeline architecture, coding standards, and ETL/ELT best practices.

Operations & DevOps

• Deploy changes through CI/CD and the Change Request (CR) lifecycle, including validation and ticket closure.
• Participate in problem management and root-cause analysis, helping drive 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 deliver curated datasets for downstream analytics consumers.
• Communicate progress, issues, and technical details clearly to engineering peers and stakeholders.
• 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 assigned data pipelines to a high standard, become comfortable with CI/CD and operational practices, and contribute to improving data quality and reducing recurring incidents — with guidance from senior engineers.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience.
• 4+ years of experience in data engineering.
• hands-on experience with Databricks, Python (PySpark), and SQL for data processing and transformation.
• Experience designing and delivering production data pipelines (ETL/ELT).
• Working knowledge of CI/CD pipelines and Git-based branching strategies.
• Familiarity with cloud platforms (AWS preferred) and core data services.
• Experience supporting production data pipelines, including monitoring, alerting, and incident response.
• Good communication skills across engineering and business audiences.

Preferred Qualifications

• Exposure to orchestration frameworks and streaming technologies.
• Familiarity with Infrastructure-as-Code and modern deployment tooling.
• Awareness of observability tooling for data platforms.
• Background in semiconductor manufacturing or large-scale industrial data processing.
• Databricks Certified Data Engineer Associate certification is a plus.

Competencies

• Ownership mindset — accountable for the quality of your pipelines, from build to production support.
• Problem-solving orientation — bias toward permanent fixes and automation.
• Growing technical depth — strong hands-on engineering and attention to quality.
• Collaboration — works well with Reporting, Platform, and Business teams across geographies.
• Clear communication — able to explain technical details to peers and stakeholders.

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