Hafiz Shehbaz Ali

I led the development of an enterprise lakehouse that unified data from over 80 systems and more than 3,000 datasets, creating a single trusted platform for financial, risk, and operational analytics. The platform supported ingestion from files, APIs, SFTP, and NFS, loading all sources into a centralized data lake and Iceberg‑based lakehouse. A scalable ingestion and processing framework standardized onboarding, validation, and governance with strong quality checks and audit trails.

I designed a full observability layer that captured technical and operational metadata, including job metrics, run histories, and dependency tracking. The system detected anomalies across pipelines, triggered automated alerts to NOC teams, and retained metadata for monitoring dashboards and operational insights. This ensured reliability, transparency, and early detection of issues across thousands of daily jobs.

The lakehouse became the organization's core data foundation for analytics, regulatory reporting, and advanced data products. The platform now provides a scalable, governed, and dependable environment for all enterprise data, enabling faster and more confident decision‑making.

I built an enterprise risk data repository that consolidated key retail and corporate risk datasets into a single, regulation-ready platform aligned with CBUAE Circular 153/2018. Daily feeds from core banking, cards, retail finance, corporate lending, collateral systems, and credit score providers such as AECB and Moody's CreditLens were cleaned, validated, and tracked with full lineage. The repository supported expected credit loss reporting, portfolio risk analysis, and marketing models, improving data quality and decision-making across risk, finance, and business teams.

Alongside the repository, I led the onboarding of the AML SAS cloud platform by designing a unified compliance data model that merged customer and transaction data from core banking, cards, loans, wealth, digital channels, and screening systems. I built scalable ingestion pipelines to standardize high-volume datasets, synchronize historical and incremental loads, and ensure alignment with AML SAS schema and regulatory expectations. This integration strengthened the bank's compliance posture, improved detection accuracy, and enabled faster investigation workflows while maintaining stability across upstream systems.

I built a pre-approved lending decisioning platform that automated how the bank evaluates customer eligibility for retail lending. The system combined salary information, liabilities, AECB data, behavior patterns, and compliance checks to deliver quick and consistent decisions across mobile, WhatsApp, and call‑center channels. This reduced manual reviews and enabled customers to receive faster responses.

The platform ran on daily PySpark and SQL pipelines with a rules‑based configuration layer, making it easy for credit‑risk and marketing teams to update eligibility logic without code changes. A dedicated pre‑production environment allowed teams to test and validate new rules before rollout, improving accuracy and reducing rework. Strong data‑quality checks and audit tracking ensured every decision aligned with CBUAE and Sharia requirements.

Analytics identified customer groups most likely to accept offers and helped match products to customer needs. The platform delivered a 70% increase in sales conversions, expanded the eligible customer base, and improved uptake across quick finance, personal finance, card finance, top-up, advance salary, easy cash installment, balance transfer, and cash on call.

I engineered a retail loyalty rewards data product that consolidated all customer reward activities across multiple banking and partner programs into a single, consistent data foundation. The platform unified customer registration, profile updates, reward portfolios, and transaction‑level activity, enabling teams to rely on a single, accurate view of each customer's rewards.

I designed and developed data pipelines that generated customer reward profiles, created detailed transaction datasets, and performed full reconciliation of reward accruals and redemptions. These pipelines ensured accuracy, traceability, and timely updates across all reward types. The solution also integrated batch‑based vendor data and supported end‑to‑end API connectivity for mobile‑app dashboards and redemption workflows.

The platform scaled to support Etihad Miles, Skywards Miles, MAF rewards, Exceed points, cashback cards, and a wide range of banking products, including salary, personal finance, auto finance, home finance, insurance, and remittance. This improved customer experience, reduced manual reconciliation, and enabled more reliable reporting and insights for loyalty teams.

I constructed a digital controls monitoring system that automated key operational and compliance checks across the bank. The platform processed daily data from more than 20 source systems and applied clear validation rules to detect missing, invalid, or unusual records. It replaced manual Excel‑based reviews and gave teams a dependable, automated way to track control health.

The solution used a structured data flow with dedicated layers for raw data, cleaned data, shared logic, anomaly detection, and reporting. Each layer included strong data‑quality checks, audit trails, and lineage tracking. More than 100 controls were automated across onboarding, KYC, payments, manual postings, cheque handling, branch operations, account activity, and financing.

Dashboards highlighted breaches, SLA delays, and repeated issues, while automated alerts notified teams when immediate action was needed. The platform reduced processing time by nearly 80%, improved compliance, and helped branches and back‑office teams resolve issues faster. It also scaled easily as transaction volumes grew, ensuring consistent monitoring across the bank.

I created a customer‑insights ecosystem, starting with a voice of customer (VoC) platform that delivered 39 monthly surveys to nearly two million customers across email and mobile channels. Using data from 20 internal systems, it created a unified view of satisfaction across onboarding, branches, digital banking, and contact‑center journeys, helping product, service, and operations teams identify issues and improve customer experience at scale.

I also integrated Visa's portfolio management platform, combining card‑spend behavior and customer activity into a single analytical view for segmentation, spend analysis, and targeted campaigns. In parallel, I delivered a personal finance manager (PFM) solution that categorized transactions, generated spending insights, and tracked budgets within the mobile app to improve financial awareness and engagement.

To support investment and wealth teams, I designed analytics data marts that unified holdings, transactions, product performance, and portfolio movements across trading, investment, and core‑banking systems. Together, these platforms strengthened customer experience, digital engagement, and data‑driven decision‑making across payments, wealth, and retail banking.

I directed the migration of the bank's on-premises Cloudera platform to Cloud CDP, redesigning over 2,000 ETL jobs and migrating nearly 2,000 business-critical tables using a hybrid sidecar and in-place upgrade strategy. This approach kept all workloads stable while new pipelines were validated in parallel. I built synchronization scripts to maintain downstream continuity and collaborated with infrastructure, security, and business teams to ensure a seamless transition. Before sunset, I archived historical datasets and executed a controlled decommissioning of the legacy platform.

In parallel, I modernized the bank's entire payment landscape by replacing legacy SWIFT and Intellect systems with PayHub, IPI, FTS, and IPP/AANI. The migration covered deep integrations across digital banking, compliance, reconciliation, settlement, and open banking partners. I supported the full AANI rollout, QR payments, merchant onboarding, VAT statements, and customer notifications. For international payments, I implemented Corridor API integrations to ensure fast, compliant cross-border flows. This modernization reduced operational risk, improved transaction speed, and enabled richer digital payment experiences for customers and merchants.

I crafted a city‑wide smart mobility analytics platform for RTA Dubai that unified multimodal datasets across parking, taxis, buses, rail, GPS, sensors, ticketing, and event systems. Using a medallion‑layer architecture and scalable PySpark pipelines, the platform processed billions of records to deliver forecasting, operational insights, and long‑term planning intelligence. Parking occupancy forecasting predicted daily and 7‑day availability using geospatial clustering and Prophet models. At the same time, the real‑time taxi demand engine is refreshed every five minutes to forecast 5‑, 15‑, and 60‑minute windows for fleet optimization.

The platform also delivered first- and last-mile connectivity analysis by integrating rail, bus, taxi, tram, and NOL data to identify multimodal gaps and guide feeder route planning. A travel time index module measured corridor congestion using GPS and sensor data, and bus network analytics improved punctuality and reduced fare evasion through route delay detection and anomaly modeling. All insights were visualized through MicroStrategy dashboards, giving planners and operations teams real‑time visibility into demand hotspots, congestion patterns, and network performance across the city.

I engineered and enhanced multiple SaaS and enterprise platforms, including a cross‑platform workforce monitoring system built in Java with automated builds, activity tracking, offline sync, and a Laravel/MySQL back end for multi‑level workforce analytics and timesheet automation. I also contributed to EventBuizz, improving event management modules, optimizing workflows, and ensuring production stability for global users. Additional work included building a custom CRM for aesthetic clinics with lead tracking, customer lifecycle management, and a dynamic form builder with embeddable SDKs.

I developed a full grocery eCommerce platform using Laravel microservices, real‑time notification queues, and an Ionic hybrid mobile app for ordering and delivery tracking. I provided long‑term support for PHP, Magento, WordPress, and AWS‑based applications, delivering performance improvements and UI fixes. I also enhanced ArtConnect's creative community platform using Symfony, Angular, Docker, and Nginx, improving stability, routing, and user workflows. At scale, I designed Rasbree, a retail operating system with Shopify GraphQL integration, supporting over 500 merchants across inventory, order management, warehouse operations, and analytics.

I designed and built a data lakehouse for Rasbree to deliver hourly, merchant-level eCommerce reporting that extends beyond Shopify Plus’s limited capabilities. The goal was to offload expensive analytical queries from the production MySQL database and deliver scalable descriptive analytics for 500+ merchants.

The solution was implemented on Databricks using Delta Lake and a medallion architecture (bronze, silver, gold). Data was ingested from application databases, unstructured sources, and system logs. Bronze stored raw data, silver applied cleaning and transformations, and gold produced curated merchant-specific datasets. These gold-layer outputs were pushed back into MySQL to power fast dashboards and hourly insights, including sales trends, product performance, inventory movement, and operational KPIs.

This architecture significantly reduced reporting costs, improved performance, and enabled merchants to access near real-time analytics that Shopify Plus does not provide, including multi-source aggregation and hourly refresh cycles.