Lead Data Engineer

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/CDKey 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,...