Lead Data Engineering - Platform Ops

Lead Data Engineering – Platform Ops

Lead Data Engineering – Platform Ops in Dubai, United Arab Emirates is a senior Financial Services technology opportunity for an experienced data engineering leader with strong hands‑on expertise in Databricks, Azure Data Factory, Kafka, Confluent, dbt, DataOps, Unity Catalog, and enterprise data platform operations. This role is ideal for a platform‑focused professional who can build, scale, secure, and operate a high‑performance data platform that supports analytics, AI, regulatory reporting, and digital transformation across a banking environment.

Job Snapshot

Country: United Arab Emirates
City: Dubai
Industry: Financial Services
Function: Information Technology
Salary: 35,000–50,000 (estimated range; final offer confirmed with the employer)

Key Responsibilities

• Own the engineering, operation, and continuous improvement of RAKBANK’s enterprise data platform.
• Design, build, and operate scalable batch and real‑time data pipelines using Azure Data Factory, Kafka, CDC, and related technologies.
• Build and manage the Databricks lakehouse platform across bronze, silver, and gold data layers.
• Ensure the data platform delivers reliable, secure, high‑quality, and high‑performance data for analytics, AI, reporting, and business decision‑making.
• Implement DataOps practices including CI/CD pipelines, automated deployments, monitoring, incident management, and operational controls.
• Develop and maintain dbt transformation models aligned with business data requirements and enterprise data standards.
• Manage real‑time streaming use cases using Kafka, Confluent, CDC, and event‑driven data architecture.
• Implement data governance through Unity Catalog, including data security, lineage, metadata management, access controls, and policy enforcement.
• Drive platform reliability by defining and managing SLAs, platform performance metrics, availability standards, and operational KPIs.
• Optimise data platform performance across ingestion, transformation, storage, compute, streaming, and downstream consumption.
• Own cloud data platform cost optimisation through FinOps practices, usage monitoring, resource efficiency, and cost governance.
• Collaborate with AI and analytics teams to enable advanced data products, machine learning use cases, and enterprise analytics capabilities.
• Support regulatory reporting needs by ensuring trusted data pipelines, strong controls, data quality, and traceability.
• Partner with cloud, cyber security, infrastructure, governance, risk, and business teams to maintain secure and compliant platform operations.
• Lead, mentor, and develop a high‑performing data engineering team.
• Establish engineering standards, best practices, reusable patterns, documentation, and delivery discipline across the data engineering function.
• Troubleshoot platform issues, manage incidents, conduct root cause analysis, and implement preventive improvements.
• Support the bank’s data transformation roadmap by enabling scalable, governed, and future‑ready data capabilities.
• Maintain strong stakeholder communication across technology, business, analytics, AI, compliance, and leadership teams.

Ideal Profile

• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field.
• 10 years of experience in data engineering, data platforms, analytics engineering, or enterprise data technology.
• Proven leadership experience managing data engineering teams, platform operations, or enterprise data delivery.
• Strong hands‑on expertise in Databricks, Azure Data Factory, Kafka, Confluent, and dbt.
• Experience building and operating Databricks lakehouse platforms across bronze, silver, and gold architecture layers.
• Strong understanding of batch data pipelines, real‑time streaming, CDC patterns, data ingestion, transformation, and data modelling.
• Practical knowledge of Unity Catalog, data governance, lineage, data security, access controls, and metadata management.
• Strong understanding of DataOps, CI/CD, monitoring, release management, incident management, and platform reliability practices.
• Experience working in banking, financial services, or another regulated environment is preferred.
• Good understanding of regulatory reporting data needs, data quality controls, auditability, and secure data operations.
• Experience with cloud cost optimisation, FinOps practices, compute efficiency, and platform usage governance.
• Ability to collaborate with AI, analytics, business intelligence, compliance, cyber security, infrastructure, and business teams.
• Strong problem‑solving skills with the ability to manage high‑priority incidents and complex platform challenges.
• Excellent communication, stakeholder management, mentoring, and technical leadership skills.
• Ability to balance hands‑on engineering depth with strategic platform ownership and team leadership.

Skills Set

• Data engineering
• Platform operations
• Enterprise data platform
• Databricks
• Databricks lakehouse
• Azure Data Factory
• Kafka
• Confluent
• CDC pipelines
• dbt
• DataOps
• CI/CD
• Unity Catalog
• Data governance
• Data lineage
• Data security
• Access controls
• Batch data pipelines
• Real‑time data pipelines
• Streaming data
• Bronze layer
• Silver layer
• Gold layer
• Data transformation
• Data modelling
• Analytics engineering
• AI data enablement
• Regulatory reporting data
• Data quality
• Platform monitoring
• Incident management
• SLA management
• Performance optimisation
• Cloud cost optimisation
• FinOps
• Banking technology
• Financial services data
• Team leadership
• Stakeholder management
• Root cause analysis
• Enterprise analytics
• Digital transformation