Lead Data Engineer

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.

Databricks · Python (PySpark) · SQL · Data Pipelines · CI/CD

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.

• 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.

• 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.

• 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.

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