A technology-forward organization in Dubai, UAE is seeking a highly experienced Senior Databricks Engineer to lead the modernization of its Databricks platform and drive the adoption of advanced Databricks features across a large-scale enterprise data environment. This senior individual contributor and technical leadership role requires a minimum of 6+ years in data and platform engineering with deep, hands-on Databricks expertise covering PySpark, Streaming, DBR, Lakeflow, Unity Catalog (UC), DQX, DB Genie, and Delta Lake. You will design and deliver scalable Lakehouse solutions, implement and optimize AI/BI workloads, establish engineering best practices and governance frameworks, and mentor engineering teams while guiding technical decision-making across the organization. Experience with Azure Data Factory (ADF), Azure Synapse, and Azure DevOps is required. Proven leadership in large-scale Databricks or Lakehouse transformation projects is essential.
About the Role — Senior Databricks Engineer, Dubai UAE 2026
Role Type: Senior Individual Contributor + Technical Leadership | Data Platform Modernization | Databricks Lakehouse Transformation
Platform Scope: Databricks platform modernization · Lakeflow pipelines · Unity Catalog governance · DQX data quality · DB Genie AI/BI · Delta Lake optimization · Streaming architectures
Azure Ecosystem: Azure Data Factory (ADF) · Azure Synapse Analytics · Azure DevOps — comprehensive Azure data platform integration
Leadership Dimension: Engineering best practices · Governance frameworks · Automation standards · Team mentoring · Cross-organizational technical decision guidance
Transformation Focus: Large-scale Databricks and Lakehouse transformation — platform modernization from legacy data architectures to enterprise-grade Lakehouse patterns
Why This Senior Databricks Engineer Role in Dubai Is a Career-Defining Opportunity
Platform Modernization Leadership: Leading large-scale Databricks Lakehouse transformation at an enterprise level — owning the architectural decisions, engineering standards, and governance frameworks that define how an organization’s entire data platform operates — is the highest-impact, most career-advancing technical role available to senior data platform engineers in the Dubai market
Cutting-Edge Databricks Stack: Working hands-on with Databricks’ most advanced and recently released capabilities — Lakeflow, DQX, DB Genie, and AI/BI workloads — places this role at the absolute frontier of the Databricks platform, building the rare and rapidly valuable technical credentials that the market is actively seeking
Technical Leadership Scope: The combination of deep individual technical contribution with cross-organizational technical mentoring and decision-making guidance creates the career-advancing combination of technical depth and organizational influence that distinguishes Principal and Staff Engineer career trajectories from senior individual contributor roles
Tax-Free Dubai: Highly competitive senior data engineering compensation — zero personal income tax in Dubai, UAE
Position Overview
This Senior Databricks Engineer role in Dubai is a full-time, senior technical position at the center of an enterprise Databricks platform modernization program. You will lead the modernization of the organization’s Databricks platform and drive adoption of advanced Databricks features, design and deliver Lakeflow jobs, data pipelines, and scalable Lakehouse architecture solutions, implement and optimize Unity Catalog governance frameworks, DQX data quality management, DB Genie AI-assisted data engineering capabilities, and AI/BI workloads on Databricks, establish engineering best practices, governance standards, and automation frameworks that raise the quality and consistency of data engineering practice across the organization, mentor engineering teams on Databricks best practices and Lakehouse architecture patterns, and guide technical decision-making across the data platform function. The role demands 6+ years of data and platform engineering experience, deep Databricks expertise across PySpark, Streaming, DBR, Lakeflow, Unity Catalog, DQX, DB Genie, and Delta Lake, proven experience with ADF, Azure Synapse, and Azure DevOps, and demonstrated leadership in large-scale Databricks or Lakehouse transformation programs.
Why This Senior Databricks Engineer Role Is the Data Platform Career of 2026 in Dubai: Senior data platform engineers with 6+ years of deep Databricks expertise — including hands-on experience with PySpark, Streaming, Delta Lake, Unity Catalog, Lakeflow, DQX, and DB Genie, proven Azure ADF and Synapse integration capability, demonstrated leadership in large-scale Databricks or Lakehouse transformation programs, and the technical mentoring and cross-organizational influence skills that senior data platform roles demand — represent one of the most genuinely rare, most rapidly advancing, and most competitively compensated technical talent profiles in Dubai’s growing enterprise data platform market. This role’s combination of cutting-edge Databricks stack breadth, Lakehouse transformation leadership scope, and AI/BI workload ownership makes it one of the most technically prestigious data engineering opportunities available in the UAE in 2026.Key Responsibilities
Databricks Platform Modernization & Advanced Feature Adoption
- Lead the modernization of the organization’s Databricks platform — directing the systematic upgrade, configuration optimization, and architectural evolution of the enterprise Databricks environment from its current state to a fully modernized, Lakehouse-architecture-aligned platform that leverages Databricks’ latest capabilities including Databricks Runtime (DBR) optimization, workspace governance through Unity Catalog, and the full range of advanced Databricks features that the platform now offers to enterprise data engineering teams
- Drive the adoption of advanced Databricks features across the engineering organization — developing the internal technical enablement, documentation, and hands-on guidance that successfully transitions engineering teams from older Databricks usage patterns to the more powerful, more governed, and more analytically capable approaches that features like Lakeflow, Unity Catalog, DQX, and DB Genie enable, managing the change management dimension of platform modernization alongside its technical implementation
- Design and deliver Lakeflow jobs and data pipelines at enterprise scale — implementing Databricks Lakeflow’s declarative pipeline authoring, managed pipeline execution, and pipeline observability capabilities to build the next generation of the organization’s data ingestion, transformation, and delivery pipelines, replacing older Spark job patterns with Lakeflow’s more maintainable, more observable, and more reliably executing pipeline architecture
Lakehouse Architecture — Delta Lake, Unity Catalog & Data Quality
- Design and implement scalable Lakehouse architecture solutions — applying Databricks Lakehouse design patterns to architect the data storage, access, governance, and compute layer configuration that makes the organization’s data Lakehouse genuinely capable of serving both data engineering, data science, and business analytics use cases efficiently and reliably, applying Delta Lake’s ACID transaction capabilities, time travel features, and Z-ordering optimization to create a reliable, query-efficient data foundation
- Implement and optimize Unity Catalog across the Databricks environment — configuring Unity Catalog’s metastore hierarchy, catalog and schema organization, data access control policies, data lineage tracking, and audit logging to establish the comprehensive data governance framework that makes the organization’s Databricks environment compliant with enterprise data governance requirements and capable of supporting self-serve analytics without governance compromise
- Implement DQX data quality management — designing and deploying Databricks DQX data quality rules, quality metrics dashboards, and quality failure alerting that systematically monitors and enforces data quality standards across the Lakehouse data assets, ensuring that data quality issues are detected, documented, and resolved at source rather than discovered by end users and analysts working with incorrect or incomplete data downstream
- Optimize streaming architectures using Databricks Structured Streaming and Spark Streaming — designing the real-time data ingestion, transformation, and delivery pipelines that process high-velocity event streams reliably and at low latency, applying watermarking, checkpointing, stateful aggregation, and streaming-to-batch unification patterns to build streaming pipelines that perform reliably in production without accumulated processing lag or state store overflow
AI/BI Workloads — DB Genie & Analytics Intelligence
- Implement and optimize DB Genie AI-assisted analytics capabilities — deploying and configuring Databricks’ AI/BI dashboard and DB Genie natural language query interface to give business stakeholders the ability to query the organization’s Lakehouse data using conversational natural language questions rather than requiring SQL expertise, building the semantic layer, table and column documentation, and example question training that makes DB Genie responses accurate and trustworthy for specific business domains
- Design and deliver AI/BI workloads on the Databricks Lakehouse platform — implementing the MLflow experiment tracking, model training, feature engineering, model serving, and model monitoring components that make the Databricks Lakehouse the unified platform for both data engineering and machine learning model development and deployment, enabling data scientists and ML engineers to work within the same governed, scalable data environment as the data engineering team
Engineering Standards, Governance, Automation & Technical Leadership
Mentor engineering teams and guide technical decision-making across the organization — actively sharing Databricks architecture knowledge, code review insights, Lakehouse design guidance, and data engineering career development support with junior and senior engineers, influencing the technical direction of the data platform without necessarily having direct management authority over every team that the platform serves
Establish engineering best practices, governance frameworks, and automation standards — developing the coding standards, code review processes, testing frameworks, CI/CD pipeline patterns, infrastructure-as-code approaches, and data engineering quality benchmarks that raise the consistency and quality of data engineering practice across the entire Databricks engineering organization, producing the written standards and reference implementations that allow teams to adopt best practices efficiently
Integrate with Azure Data Factory (ADF) and Azure Synapse Analytics — designing and implementing the orchestration patterns, data movement workflows, and integration architectures that connect the Databricks Lakehouse with the broader Azure data platform ecosystem, including ADF pipeline orchestration of Databricks notebooks and jobs, Synapse Analytics integration for specific analytics workloads, and Azure DevOps pipeline automation for Databricks deployment and testing
Qualifications & Requirements
Core Requirements
- 6+ years of experience in data and platform engineering — with deep, demonstrated Databricks expertise including production delivery on Databricks across PySpark, Streaming, DBR, Delta Lake, and Unity Catalog at enterprise scale
- Strong hands-on experience with Lakeflow, UC, DQX, and DB Genie — these are the advanced Databricks features that define the platform modernization agenda and must be demonstrated through real implementation experience rather than theoretical familiarity
- Proven experience with ADF, Azure Synapse, and Azure DevOps — for cross-platform Azure data ecosystem integration and CI/CD deployment automation
- Demonstrated leadership in large-scale Databricks or Lakehouse transformation projects — with evidence of successfully guiding an organization through the complexity of enterprise-scale data platform modernization
About Databricks Engineering & Enterprise Data Platform Careers in Dubai 2026
The Databricks Lakehouse platform has emerged as the de facto enterprise data platform standard for organizations that want to unify data engineering, data science, and business analytics on a single, governed, and scalable data foundation — and Dubai’s enterprise technology market is actively investing in this architecture transition in 2026. The Senior Databricks Engineers who lead these transformations — who can design the Unity Catalog governance model, architect the Lakeflow pipeline patterns, implement DQX data quality frameworks, and guide organizations through the technical complexity of migrating from fragmented legacy data environments to coherent, well-governed Lakehouse architectures — are defining what enterprise data platforms will look like across the UAE’s financial services, retail, government, healthcare, and technology sectors for the next decade. The combination of Databricks’ accelerating platform innovation (Lakeflow, DB Genie, DQX, and AI/BI workloads represent genuinely transformative new capabilities), the UAE’s active enterprise cloud adoption agenda, and Dubai’s outstanding professional lifestyle and tax-free financial environment makes this senior Databricks engineering role one of the most technically stimulating, commercially significant, and personally rewarding data platform engineering opportunities available in the Middle East in 2026.
Your Career Growth Path: Senior Databricks Engineer → Principal Data Engineer → Staff Engineer — Data Platform → Head of Data Engineering → Director of Data Platform → VP Data & Analytics — a globally recognized, technically frontier, and commercially consequential data platform engineering career trajectory built at the leading edge of the Databricks Lakehouse ecosystem in one of the world’s most dynamically evolving enterprise technology markets.
Who Should Apply?
- Senior Databricks Engineers (6+ Years): With deep production PySpark, Delta Lake, Unity Catalog, and Lakeflow experience and demonstrated Lakehouse architecture and governance expertise who want a Dubai-based senior platform engineering role with organizational technical leadership scope
- Data Platform Architects — Databricks Specialists: With Lakehouse architecture design experience and enterprise Databricks governance implementation track records who want a hands-on senior engineering role combining deep technical contribution with platform modernization leadership in Dubai
- Azure Data Engineers — Databricks Focus: With comprehensive Azure data platform backgrounds (ADF, Synapse, Azure DevOps) and deep Databricks engineering expertise who want a senior role that combines both their Azure ecosystem knowledge and Databricks specialization in a large-scale modernization context
- Principal/Staff Data Engineers — Databricks Platform: With organizational technical leadership experience across engineering standards, team mentoring, and cross-functional technical decision-making on Databricks Lakehouse programs who want a senior engineering role in Dubai’s growing data platform market
- Databricks Lakehouse Transformation Leaders: Who have successfully led the migration of legacy data warehouse or data lake architectures to Databricks Lakehouse patterns at enterprise scale and want to apply that transformation leadership experience in a senior Dubai-based role with clear organizational influence and advanced platform scope
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