Data Analytics Manager | SQL • Python • Power BI | Dubai, UAE

I build reliable analytics products end-to-end: from messy source files and databases to automated pipelines, governed datasets, and decision-ready dashboards. My work typically sits at the intersection of business operations and engineering—turning manual reporting and fragmented processes into scalable, auditable systems.

• Analytics leadership: manage and coach analysts, set SLAs/KPIs, improve delivery quality and ownership.
• Data engineering (pragmatic): ingestion from Excel/CSV/APIs, cleaning, validation rules, automation, scheduling.
• BI & semantic models: Power BI models and reports used by management and large user groups.
• Automation: Python orchestration scripts, monitoring, email notifications, and repeatable refresh workflows.
• Process digitization: lightweight web tools (often Python/Django) that replace manual inputs and spreadsheet chaos.

Languages & Querying

• Python (pandas, automation, APIs, web scraping)
• SQL (performance tuning, complex queries, data modeling)

BI / Reporting

• Power BI (DAX, modeling, governance, report performance)

Data & Storage

• SQLite / Postgres (and other relational concepts)
• File-based pipelines (Excel/CSV/JSON), data quality checks, reproducible transformations

Automation & Tooling

• CLI tools, schedulers, Windows-based automation
• Git / GitHub workflows (branching, pull requests, repo hygiene)

• Synthetic data generation for demos, testing, and analytics prototyping when real data cannot be shared
• CRM / sales analytics: customer segmentation, cohort behavior, basket logic, retention signals
• Operational reporting automation: consolidate multi-source inputs, validate, load, publish, notify
• Data quality tooling: rule-based validation, anomaly detection, consistent column normalization for “real-world messy Excel”
• Web interfaces for business processes: controlled inputs, approvals, audit logs, and API access

• Prefer simple, maintainable solutions over over-engineering.
• Value data quality and auditability: clear transformations, repeatable runs, and traceability.
• Focus on business outcomes: reduce manual effort, accelerate decisions, and improve reliability.
• Write documentation so others can onboard quickly and operate with confidence.

Most of my projects follow a simple pattern:

• src/ – core code (pipelines, business logic, utilities)
• data/ – sample inputs (or placeholders if data cannot be shared)
• output/ – generated exports (often gitignored)
• README.md – usage, assumptions, and examples

If a project uses a local database (*.db, *.sqlite), it is usually excluded from Git history unless it is fully synthetic and reproducible.

• LinkedIn:
• GitHub:
• Location: Dubai, UAE

If you are reviewing this repository for hiring or collaboration:

• I’m happy to clarify assumptions, dataset design decisions, or architecture trade-offs.
• Where real company data cannot be shared, you will see synthetic datasets that preserve structure and realism without exposing confidential information.