Data Engineer · SQL Server & ETL · Azure Data Infrastructure & Reliability
Building reliable, observable, and governed data systems.
I’m a Data Engineer with a strong foundation in SQL Server, ETL, data infrastructure, and operational reliability, now focused on Azure Data Engineering.
My work centers on a simple idea: data platforms should be predictable under normal conditions, observable when they fail, recoverable when something goes wrong, and trustworthy for downstream consumers.
Across the portfolio, I work with ingestion, orchestration, Lakehouse patterns, analytical serving, real-time processing, production readiness, and governance — with reliability as the common thread.
| Project | Engineering focus |
|---|---|
| Azure Real-Time Analytics Pipeline | Event Hubs, KQL, Raw/Parsed/Canonical modeling, stream integrity, timeliness, reconciliation, state reconstruction, operational observability |
| Production-Ready Azure Data Pipeline | ADF, Managed Identity, RBAC, Key Vault, Bicep, GitHub Actions, Log Analytics, KQL diagnostics, Azure Monitor alerts |
| Azure Databricks Delta Lakehouse | PySpark, Delta Lake, Bronze/Silver/Gold, MERGE, SCD Type 2, Time Travel, data quality |
| Azure ADF Incremental Ingestion Framework | Metadata-driven orchestration, SQL Server integration, watermarks, incremental loading, retries, operational controls |
| SQL Server Recovery & Validation Framework | Backup scheduling, restore-chain planning, PITR, marked transactions, canary validation, recovery telemetry |
| Azure Data Governance & Lineage POC | Microsoft Purview, discovery, classification, stewardship, glossary metadata, Managed Identity, ADF lineage |
Reliable data infrastructure
Traceability, recoverability, data quality, observability, failure handling, operational clarity.
Azure Data Engineering
ADF, ADLS Gen2, Databricks, Delta Lake, Synapse Serverless SQL, Event Hubs, KQL, Azure Monitor, Microsoft Purview.
SQL Server & ETL
T-SQL, SSIS, performance tuning, backup/recovery, HA/DR, production support, operational troubleshooting.
Production-aware engineering
Managed Identity, RBAC, Key Vault, Infrastructure as Code, CI validation, diagnostics, alerting, and cost-aware resource decisions.
SQL Server · T-SQL · SSIS · Python · Azure Data Factory · ADLS Gen2 · Databricks · PySpark · Delta Lake · Synapse Serverless SQL · Event Hubs · KQL · Azure Monitor · Microsoft Purview · Bicep · GitHub Actions
- Make failures visible instead of hiding them.
- Preserve enough evidence to explain what happened.
- Design recovery paths before they are needed.
- Separate physical delivery from logical truth.
- Keep business logic reusable and presentation layers thin.
- Prefer explicit scope boundaries over inflated claims.
- Azure Synapse Serverless Serving Layer — analytical serving over ADLS Gen2 with external tables, views, CETAS, and cost-aware SQL.
- Azure Event-Driven Data Pipeline — foundation event-driven pipeline with Event Hubs, Azure Functions, layered validation, current-state modeling, and Gold aggregation.
- Tableau Public — analytics and visualization work.
My engineering background predates the Azure portfolio.
I’ve worked with SQL Server administration, ETL, production databases, performance tuning, backup and recovery, infrastructure, and operational support. The Azure work extends that same reliability mindset into modern data platforms rather than replacing it.