Data Engineer

Location: Dubai

Work Arrangement: Standard workdays (Monday to Friday) & hours (9am to 5pm)

Engagement: Full-time, 3-month initial contract

Seniority: 5 years, hands-on across data, controls and Power BI

About the Role

This role exists to make a small number of high-effort, business-critical recurring reports trustworthy and repeatable - while establishing minimum viable data ownership and quality controls over the data those reports depend on. The approach is deliberately lean: controlled extracts and repeatable transformation logic, not a new enterprise data platform.

What You'll Deliver

• A prioritized critical-data-element register, business glossary, ownership matrix, and source-of-truth documentation for the agreed scope
• Documented data quality rules (completeness, validity, allowed values, duplicates, thresholds, severity, ownership, remediation)
• A recurring-report inventory, narrowed to 2–3 reports selected on business value, manual effort, and feasibility
• Controlled XLSX/CSV or read-only source extracts with repeatable Power Query/Python transformation logic, record counts, control totals, error logging, archiving and versioning
• 2–3 production-quality Power BI (or GCP/alternative) reports/dashboards with governed KPI definitions, semantic models, DAX, access controls, and scheduled or owner-triggered refresh
• Runbooks, data dictionaries, and test evidence sufficient for a lean internal team to take over maintenance

Key Responsibilities

• Identify which reports, decisions and processes are most exposed to unreliable or manually-assembled data, and profile the underlying extracts
• Resolve conflicting KPI definitions with business owners before build, and document the agreed definitions, owners, exceptions and limitations
• Build resilient transformations that hold up against inconsistent file extracts - changing headers, column order, formats, incomplete data
• Reconcile outputs against source reports and control totals, surfacing unresolved breaks rather than masking uncertainty
• Build and release Power BI (or alternative) semantic models, measures, drill paths and visuals with proportionate refresh and role-based access
• Work with operations, finance, investments, client teams and vendors to establish sustainable extract, review and remediation routines

Must-Have Experience

• Hands-on Power BI to production standard: data modelling, DAX, refresh configuration, row-level security, workspace/access management
• Strong Power Query/M and Excel; working Python/pandas; practical SQL for read-only extraction and validation
• Exposure to GCP data reporting (Looker, BigQuery)
• Track record delivering reliable reporting without depending on a warehouse or modern cloud data stack
• End-to-end setup of a reporting platform/layer (Power BI, GCP, or equivalent)
• Practical data governance/MDM experience - personally built CDE registers, glossaries, ownership matrices, or data-quality rulebooks
• Strong reconciliation, control-total, exception management and remediation-workflow capability
• Confidence facilitating metric-definition decisions and producing documentation non-specialist owners can operate

Preferred / Advantageous

• KSA/GCC delivery experience, and familiarity with regulated data residency and privacy boundaries
• Asset management, wealth management, fund administration, or investment-reporting background (AUM, NAV, performance, holdings, fund structures)
• Exposure to eFront, Bloomberg, Oracle ERP, or comparable portfolio/market-data/finance systems
• Arabic reporting or bilingual/right-to-left layout experience

What This Role Is Not

• Not a cloud data-engineering role, and not a pure dashboard-design role - file-based transformation, governance, reconciliation and handover are all core to it
• Out of scope: building a data lake/warehouse/lakehouse, real-time pipelines, enterprise MDM/catalogue tooling, broad source-system remediation, or unrestricted self-service
• Relevant tools: Power BI, Power Query, GCP BigQuery, Looker. Azure Synapse, Azure Data Factory, Microsoft Fabric, Azure ML, Databricks, Snowflake, dbt, Airflow and Kafka are not screening requirements but may be considered a plus