Designing Modern
Data Platforms
with Microsoft Fabric & Azure
I design scalable, secure, and automated data solutions using Microsoft Fabric, Azure Data Factory, Azure Databricks, SQL, PySpark, Dataflows, Pipelines, Notebooks, and Eventstream.
Data Pipelines
Azure Data Factory · PySpark
Lakehouse Architecture
Microsoft Fabric · Delta Lake
Analytics & BI
Power BI · SQL · Notebooks
8 Core Competency Domains
From raw ingestion to trusted business insights — full-stack data engineering expertise.
Microsoft Fabric
End-to-end analytics platform — Lakehouse, Warehouse, Pipelines, Notebooks, Eventstream, and Power BI in a unified SaaS environment. Expert in workspace governance and capacity management.
Data Engineering
Architecting scalable ingestion, transformation, and serving layers for enterprise workloads. Medallion architecture expert.
Azure Data Factory
Complex orchestration pipelines, parameterized frameworks, trigger strategies, and CI/CD integration for enterprise ETL.
SQL Engineering
Advanced T-SQL, stored procedures, query optimization, indexing strategies, and dimensional modeling for analytical workloads.
ETL/ELT Pipelines
Designing fault-tolerant, idempotent data pipelines with PySpark transformations, watermark patterns, and automated error handling at scale.
Enterprise Integration
Connecting SAP, OpenText, REST APIs, and legacy systems to modern Azure data platforms with robust schema mapping.
Data Modeling
Star schema, snowflake, and hybrid models tuned for BI performance and governed semantic layers.
Data Governance
Implementing Microsoft Purview for enterprise data cataloging, lineage tracking, sensitivity labels, and automated classification policies.
Consulting Engagements
Real-world enterprise data challenges solved with Microsoft Fabric and Azure.
Grandeur Properties International – The Global Listing Intelligence Initiative
Business Challenge
Manual consolidation of nightly listing files from London, Dubai, and New York caused 24–48 hour latency, copy-paste duplication errors, and zero audit trail — risking multi-million pound portfolio decisions on stale data.
Key Outcomes
Reporting latency reduced from 48 hrs → 7 AM daily · 10+ hrs/week analyst effort eliminated · 100% audit timestamp coverage · Zero duplicate records via upsert key
Global Freight Forwarders – Logistics Data Modernization
Business Challenge
Manual JSON shipment log ingestion relying on OS file timestamps caused a confirmed SLA breach in February 2026 — a 47-minute post-cutoff file went undetected for 18 hours, triggering a formal client escalation from Oceanic Freight.
Key Outcomes
Latency reduced from 4–6 hrs → minutes · 12–15% manual error rate eliminated · Full Delta Lake audit trail · Architecture scales to 10× log volume growth
City of Metropolis – Solving the Last Mile Problem using Microsoft Fabric
Business Challenge
Manual file triage across three municipal departments (Police, Parking, 311) caused 2–4 hour ingestion delays, duplicate processing, ghost files, and zero quarantine mechanism — with three hardcoded pipelines that could not scale.
Key Outcomes
95% reduction in manual effort · 100% file routing accuracy · Zero files remaining in Landing Zone post-run · Single dynamic router replaces 3 hardcoded pipelines
Let's Build Something Remarkable
Whether you need a senior Data Engineer, an Azure architecture review, or a Microsoft Fabric implementation — I'm here to help turn complex data challenges into scalable solutions.