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