SQL Server & ETL foundations → Azure Data Engineering → Reliable, observable data systems.

Data infrastructure • Reliability • Azure • Streaming • Lakehouse • Operational analytics

I’m a Data Engineer with a long-standing background in SQL Server, ETL, data infrastructure, and operational reliability, now extending that foundation into modern Azure Data Engineering.

My portfolio follows a deliberate progression:

SQL Server / ETL / operational reliability
                ↓
Cloud ingestion and orchestration
                ↓
Lakehouse transformation and history
                ↓
Analytical serving
                ↓
Security / IaC / monitoring / alerting
                ↓
Real-time reliability / state / observability

The recurring theme across my work is reliability: knowing what arrived, what changed, what failed, what state is true, and what downstream consumers can trust.

• Data infrastructure & reliability — traceability, recoverability, data quality, observability, failure handling, and operational clarity.
• SQL Server & ETL — T-SQL, SSIS, performance tuning, backup/recovery, HA/DR, and production support.
• Azure Data Engineering — ADF, ADLS Gen2, Databricks, Delta Lake, Synapse Serverless SQL, Event Hubs, KQL, Azure Monitor, and Managed Identity.
• Production readiness — RBAC, Key Vault, Bicep, GitHub Actions validation, diagnostics, alerts, and cost-aware resource decisions.
• Real-time analytics — event quality, canonicalization, integrity vs timeliness, state reconstruction, Gold serving, and operational observability.

Real-time Azure Data Engineering pipeline using Event Hubs + KQL with Raw / Parsed / Canonical analytical layers, stream integrity and timeliness analysis, Raw-to-Canonical reconciliation, state reconstruction, Gold serving, controlled failure scenarios, and a four-page operational dashboard.

What it proves: real-time analytical engineering, stream reliability, event semantics, state modeling, and operational observability.

Production-readiness project focused on Managed Identity, RBAC, Key Vault, Bicep, GitHub Actions validation, Log Analytics, KQL diagnostics, Azure Monitor alerts, and failure handling around a small ADF ingestion pipeline.

What it proves: operational maturity beyond a pipeline that merely “works.”

Lakehouse project using PySpark + Delta Lake with Bronze / Silver / Gold layers, MERGE, SCD Type 2, Time Travel, rejected records, and validation reporting.

What it proves: modern transformation, historical modeling, data quality, and Lakehouse engineering.

Metadata-driven ADF ingestion framework integrating SQL Server and file sources with control tables, watermarks, incremental loading, retry/failure validation, and operational monitoring.

SQL serving layer over ADLS Gen2 using Synapse Serverless SQL, external tables, reporting views, data-quality checks, CETAS, and cost-aware querying.

Foundation event-driven project using Event Hub, Azure Functions, ADLS Gen2, layered validation, stateful modeling, and Gold aggregation.

Production-oriented framework for backup orchestration, restore-chain construction, restore validation, point-in-time recovery testing, and auditability.

This project represents the operational reliability mindset that also runs through my Azure portfolio.

• Energy Multimarket Dashboard — Brent vs WTI exploratory dashboard built from an automated ETL workflow.
• Additional analytics work includes customer churn, A/B testing, telecom behavior analysis, SQL analysis, and Tableau reporting.

I’m focused on Data Engineering roles centered on reliable data infrastructure, especially environments that value:

• SQL Server and ETL depth.
• Azure Data Engineering.
• Data reliability and observability.
• Production-aware pipeline design.
• Real-time and event-driven systems.
• Clear technical documentation and operational ownership.

The next technical capability on my roadmap is data governance and lineage, but my current priority is turning the completed portfolio into a concise recruiter-facing and interview-ready story.

If your team is working on data infrastructure, Azure pipelines, SQL Server modernization, streaming analytics, or reliability-heavy data systems, feel free to reach out on LinkedIn or email.