Current Focus
Data Engineering for Fintech
Currently running data and analytics at a UAE payments fintech. The data platform, real-time pipelines, and finance dashboards that sit downstream of the transactional systems.
Context
What data engineering looks like at a payments fintech
Data accuracy is non-negotiable. Every transaction ends up in a dashboard, a reconciliation report, or a finance KPI. The data platform has to be reliable under streaming load, correct across multiple entities and currencies, and understandable when someone from finance asks why a number moved.
My scope is the data platform itself: ingestion from internal systems and partner feeds, transformation, real-time pipelines, reconciliation data models, and the dashboards and alerting on top. The accounting logic, compliance program, and regulatory sign-off sit with the finance, risk, and security teams. I build what they need to do their jobs.
Capabilities
What I build for fintech
Reconciliation Data Models
Data models that let finance teams automate reconciliation work that used to live in spreadsheets. I build the pipelines and models. Finance defines the matching rules and signs off on the numbers.
- Ingestion from internal databases and partner feeds
- Normalized transaction models across sources
- Exception surfaces finance can triage
- Dashboards on top for day-to-day ops
Real-Time Transaction Pipelines
Streaming ingest of financial transactions with Spark Structured Streaming and CDC, feeding live dashboards and alerts so ops and finance see issues within minutes instead of the next morning.
- Spark Structured Streaming with CDC from operational DBs
- Multi-currency handling with live FX rates via API
- Multi-entity transaction models
- Operational dashboards and anomaly alerting
Platform Migrations
Moving off legacy ETL onto a modern platform. Delivered a Databricks migration that cut ETL cost by 50% while improving data freshness.
Finance & Ops Analytics
BI dashboards that translate transaction data into the KPIs finance, ops, and leadership actually use day to day.
Multi-Source Ingestion
Pulling together internal SQL and NoSQL databases, partner JSON feeds, and external APIs like FX rates into a single analytical layer.
Good Fit
Where this work applies
Payments and digital banking
Money transfer, wallets, payment processors, neobanks. My current engagement is in this space.
Other financial-data-heavy companies
E-commerce and marketplaces reconciling against processors, SaaS with complex billing. The data engineering work transfers even without domain specialization.
Results
Track record
50%
ETL cost reduction through a Databricks migration at a UAE payments fintech
Real-time
Streaming transaction pipelines with Spark Structured Streaming and CDC, feeding live dashboards and alerts
Finance-ready
Reconciliation data models that let the finance team automate work previously done in spreadsheets
Services
How I can help
Data Strategy
Assess your current state and plan the right architecture for your compliance and scale needs.
Learn more →Data Platforms
Build data infrastructure that fits your stage, with proper access controls and lineage via the platform tooling.
Learn more →Analytics & BI
Finance and ops dashboards built on top of trusted data models.
Learn more →Building out the data platform at a payments company?
Happy to talk through platform migrations, reconciliation models, or real-time pipelines.