Manager Data & Platform Engineering

The Senior Data Engineer will lead and mentor a Databricks engineering team while driving best-practice data development, architecture, and SDLC standards. Responsibilities include owning high-quality delivery, reviewing code, shaping technical designs, breaking down user stories, and supporting the organization’s Databricks modernization. The successful candidate will continuously improve tools, processes, and data product reliability through strong technical leadership and clear communication.

Location: Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates
Work Conditions: On-site, Full-time

Key Accountabilities:

• Execute strategy, follow engineering principles, and apply design frameworks for delivery.
• Lead and coach a team of Databricks Data Engineers.
• Develop, guide, and verify application development aligned to Databricks best practices.
• Improve and uplift data engineering standards and SDLC frameworks.
• Support Chapter activities to modernize, grow, and mature data and platform engineering capabilities.
• Perform peer code reviews and provide constructive feedback to uplift overall coding quality.
• Recommend improvements to enhance the quality and reliability of data products.
• Contribute to low-level architecture and technical design aligned with high-level solutions and business requirements.
• Analyze and decompose user stories into clear implementation tasks within the agile development team.
• Proactively identify new frameworks, tools, technology, and processes to improve development team efficiency.
• Contribute to the Databricks modernization roadmap in the organization.
• Take responsibility for the end-to-end quality of personal deliverables.
• Communicate clearly and effectively with team members throughout the implementation lifecycle.

Qualifications:

• 5+ years of experience in a data and platform engineering role or related functions.
• Bachelor's degree in a related field and relevant technology certifications.
• Strong Azure Databricks hands-on experience.
• Deep PySpark + SQL engineering capability.
• Experience building reusable frameworks and standards.
• Comfortable working inside enterprise-scale environments, with knowledge of Lakeflow pipelines, DBSQL, and Unity Catalog.
• Strong DataOps background, with experience in Azure DevOps or similar tools to support the CI/CD of data products.