Enterprise Data & Analytics Engineer

I design and build end-to-end data platforms across Microsoft Fabric and Azure Databricks, covering ingestion, medallion architecture, data quality, dimensional modeling, governance, orchestration, real-time analytics, semantic models, and CI/CD.

My work focuses on building production-style solutions that move from raw source data to governed analytical products and business-facing insights.

• Microsoft Fabric
• Azure Databricks
• Apache Spark / PySpark
• Delta Lake
• Lakeflow Jobs
• Lakeflow Spark Declarative Pipelines
• Fabric Data Factory Pipelines
• Auto Loader
• Medallion Architecture

• Unity Catalog
• Row-Level Security
• Dynamic Column Masking
• Governed Tags & ABAC
• Data Lineage
• Audit / System Tables
• OneLake
• Delta Sharing

• Power BI
• Semantic Models
• DAX
• T-SQL
• KQL
• Fabric Warehouse
• Eventhouse
• Eventstream

• Git
• GitHub Actions
• Databricks Declarative Automation Bundles
• OIDC / Workload Identity Federation
• Fabric Git Integration
• CI/CD

• Microsoft Certified: Fabric Analytics Engineer Associate — DP-600
• Microsoft Certified: Fabric Data Engineer Associate — DP-700
• Microsoft Certified: Azure Databricks Data Engineer Associate — DP-750

Currently expanding into AI engineering.

Public, synthetic enterprise data-engineering implementation built on Azure Databricks.

Highlights:

• Physical source files landed in ADLS Gen2
• Incremental ingestion with Auto Loader and checkpoints
• Bronze append history and Silver Delta MERGE processing
• Schema evolution and data-quality quarantine
• Lakeflow Job orchestration
• Lakeflow Spark Declarative Pipeline
• Gold dimensional model with facts and dimensions
• Unity Catalog governance with RLS, masking, governed tags, and ABAC
• Table and column lineage
• Audit/system-table monitoring
• Databricks Genie with benchmarked natural-language analytics
• Bidirectional Microsoft Fabric ↔ Databricks interoperability
• Declarative Automation Bundles
• GitHub Actions CI/CD using OIDC workload federation

Synthetic public-sector analytics platform demonstrating enterprise Microsoft Fabric architecture while keeping commercial implementation and data private.

Highlights:

• Bronze / Silver / Gold architecture
• Fabric Lakehouse and Warehouse
• Data pipelines and transformation workflows
• Governance and analytical modeling
• Public portfolio implementation using synthetic data

Medallion Lakehouse implementation for critical-minerals analytics.

Highlights:

• CSV ingestion
• PySpark transformations
• Star-schema modeling
• Power BI analytical reporting

End-to-end fleet analytics solution built with Microsoft Fabric.

Highlights:

• SQL Server / CSV ingestion
• Fabric pipelines
• Dimensional modeling
• Power BI reporting

Automated e-commerce supply-chain analytics pipeline.

Highlights:

• Batch ingestion
• Incremental processing
• Dataflows
• Semantic modeling
• Power BI

Real-time operational analytics implementation using Microsoft Fabric.

Highlights:

• Eventstream
• Eventhouse
• KQL
• Delta Lakehouse
• Real-time dashboards
• Analytical reporting

Distribution analytics and forecasting platform.

Highlights:

• Medallion architecture
• Distribution KPIs
• Time-series forecasting
• Executive dashboards

I am currently expanding my engineering portfolio into:

• Microsoft Foundry
• Retrieval-Augmented Generation
• AI agents
• Document intelligence
• Multi-agent systems
• AI application evaluation and observability

Email: leeroyvaillant@outlook.com