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DELIVERY EXPERIENCE · 2021—2026

Data platforms.
Production ML.

Eight selected Azure data-engineering projects delivered as an external consultant, plus three data-science and machine-learning projects built for a digital insurer.

08 External consulting · data engineering03 Data science · machine learning
Client names and sensitive implementation details are intentionally omitted. Dates, responsibilities, and representative technical scope are preserved.

Data Engineering Consultant · 2023—2026

COSMO CONSULT

Azure data-engineering engagements delivered as an external consultant—from focused platform contributions to full architecture, implementation, release, training, and handover.

08Projects

Microsoft Fabric · Automated analytics

Automated analytics platform

My role Lead Data Engineer

About the project

The project replaced manual analytics based on Excel and disconnected processes with an automated Microsoft Fabric platform. Data from three SQL endpoints—including an ERP system and an internal warehouse—was ingested in batches and transformed through a medallion architecture.

Outcome: An automated data platform supplying curated Warehouse data to automatically refreshed Power BI reports, with operational monitoring and failure alerts built in.

My contribution

  • Translated the business requirements into technical requirements and designed the complete Fabric architecture.
  • Built the batch-ingestion pipelines and medallion layers: a bronze Lakehouse followed by silver and gold Warehouses.
Core stack
Microsoft FabricSQLBatch pipelinesLakehouseFabric Warehouse

Microsoft Fabric · Centralised analytics

Central data platform for multiple departments

My role Lead Data Engineer

About the project

Three Fabric medallion platforms unified data from departmental APIs, Microsoft Dataverse, and SaaS/ERP systems. Most ingestion was batch, while a shared Warehouse supported near-real-time read and write use cases.

Outcome: Central data Warehouses used by reporting and connected systems, with CI/CD, delta history, monitoring, and automatic capacity scaling.

My contribution

  • Owned the data-engineering scope and built all three Fabric architectures, including batch, near-real-time, and delta-history patterns.
  • Built the Dev–Test–Prod CI/CD system, monitoring, capacity scaling, documentation, and continued source integrations.
Core stack
Microsoft FabricPySparkDelta loadingAzure DevOps CI/CDNear-real-time data

Microsoft Fabric · Batch & real-time analytics

Batch and real-time analytics platform

My role Lead Data Engineer

About the project

Business Central data was batch loaded into a bronze Lakehouse and silver/gold Warehouses, while Azure SQL sensor data fed a separate near-real-time Fabric Warehouse.

Outcome: Power BI-ready batch and near-real-time Warehouses with monitoring, alerts, controlled releases, and an internal team prepared to extend the platform.

My contribution

  • Owned requirements, architecture, and implementation for the batch medallion platform and near-real-time sensor workspace.
  • Built Fabric CI/CD V2 with Python and SQL packages, then delivered monitoring, documentation, training, and handover.
Core stack
Microsoft FabricPySparkSQLReal-Time IntelligenceAzure DevOps CI/CD

Azure Synapse · Batch & streaming analytics

Multi-source batch and streaming platform

My role Data Engineer

About the project

Sensor streams, SQL databases, and files were integrated through batch and real-time pipelines, first in Synapse and later as part of a migration to Fabric.

Outcome: Cleaned Warehouse data and Power BI reporting flows, with the platform progressing from Synapse toward Microsoft Fabric.

My contribution

  • Built Synapse and Airflow pipelines and developed data modules for specific source systems.
  • Contributed as an engineer within an established eight-person delivery team.
Core stack
Azure SynapseAirflowPythonAzure FunctionsBatch & streaming

Microsoft Fabric · Real-time AI analytics

Real-time computer-vision analytics

My role Data Engineer

About the project

A GPU-equipped camera device ran computer-vision models for estimated age and emotion, then streamed the results through Fabric Eventstream into a KQL Database.

Outcome: An event-ready demonstration with live on-screen characteristics and aggregate reports generated from real-time camera data.

My contribution

  • Prepared the hardware and built the real-time tracking and statistics code that generated and transmitted the events.
  • Built the Fabric streaming and KQL analytics environment and optimised the end-to-end flow for minimal delay.
Core stack
Computer visionMicrosoft FabricEventstreamKQL DatabaseReal-time analytics

Microsoft Fabric · Operational analytics

End-to-end analytics platform

My role Lead Data Engineer

About the project

Data from an on-premises SQL Server was integrated into a new Microsoft Fabric platform to replace limited analytics and provide a clearer operational overview.

Outcome: A complete Fabric analytics platform and an internal team prepared to operate and expand it after handover.

My contribution

  • Owned the architecture, SQL and PySpark implementation, technical delivery, and customer communication.
  • Delivered practical Fabric training so the internal team could operate and extend the solution.
Core stack
Microsoft FabricLakehousePySparkSQLSolution architecture

Azure Synapse · Release automation

Automated releases for a Synapse platform

My role DevOps Engineer

About the project

An established Synapse platform needed a repeatable development-to-production release process and additional engineering support.

Outcome: A repeatable release path plus the additional notebook and platform components required by the existing project.

My contribution

  • Built and supported the Azure DevOps CI/CD process for the Synapse platform.
  • Delivered targeted Databricks notebooks, Azure Functions, and Synapse engineering tasks.
Core stack
Azure SynapseAzure DevOpsCI/CDDatabricksAzure Functions

Azure Synapse · Automated reporting

SaaS-to-Power BI analytics platform

My role Data Engineer

About the project

Data from a SaaS provider arrived through Azure Storage and was batch processed in Synapse using Spark-based bronze/silver layers and a gold SQL pool.

Outcome: Analytics-ready data in the gold SQL pool connected directly to automatically refreshed Power BI reports.

My contribution

  • Co-owned the technical requirements, architecture, and implementation of the end-to-end Synapse platform.
  • Built the Azure Storage ingestion and Spark transformations through to the gold SQL pool, then completed documentation and handover.
Core stack
Azure SynapseAzure StoragePySparkSQLBatch ingestion

Data Scientist & ML Engineer · 2021—2023

GETSAFE

Production modelling and decision-support projects for a digital insurer, connecting technical delivery to measurable commercial outcomes.

03Projects

Machine learning · Lead scoring

Production lead scoring

My role Data Scientist & ML Engineer

About the project

The project introduced a production machine-learning score to help the business prioritise prospective customers instead of treating every lead equally.

Outcome: A deployed lead-scoring system that contributed to a measured 38% increase in conversion rate.

My contribution

  • Built the data collection, cleaning, and preprocessing workflow.
  • Trained and evaluated the lead-scoring model.
Core stack
PythonSQLXGBoostSnowflakeDVC

Machine learning · Customer lifetime value

Customer lifetime value for marketing decisions

My role Data Scientist

About the project

The project produced customer lifetime value estimates so marketing investment could be directed more efficiently.

Outcome: A reusable customer-value signal designed to support more selective marketing investment and reduce inefficient acquisition spend.

My contribution

  • Prepared customer and policy data for modelling.
  • Built a customer-lifetime-value model for acquisition analysis.
Core stack
PythonSQLLightGBMSnowflakeCLTV

Data science · Dynamic pricing

Dynamic pricing across products and markets

My role Data Scientist & Pricing Lead

About the project

The project focused on developing and adapting insurance pricing for five products across two markets while supporting new product launches.

Outcome: A repeatable pricing capability used across products and markets during a period of significant business growth and expansion.

My contribution

  • Led and implemented the dynamic-pricing approach across five products and two markets.
  • Built automated competitor-data and model workflows.
Core stack
PythonSQLdbtPricing analyticsWeb scraping

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