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

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

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

My portfolio extends that foundation across a complete Azure learning and implementation path:

SQL Server / ETL / operational reliability
                ↓
Event-driven ingestion
                ↓
Batch orchestration and incremental loading
                ↓
Lakehouse transformation and historical modeling
                ↓
Serverless analytical serving
                ↓
Security / IaC / monitoring / alerting
                ↓
Real-time reliability / state reconstruction / observability
                ↓
Governance / metadata / lineage / stewardship

The recurring theme across my work is reliability and trust: knowing what arrived, what changed, what failed, what state is true, where data came from, who owns it, and what downstream consumers can safely use.

• 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 — canonicalization, integrity vs timeliness, reconciliation, state reconstruction, Gold serving, and operational observability.
• Data governance — Microsoft Purview, discovery, classification, stewardship, glossary metadata, lineage, and governance validation.

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 an operational dashboard.

What it proves: real-time analytical engineering, stream reliability, event semantics, state modeling, reconciliation, 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 an 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, retries, failure validation, and operational monitoring.

Role in the portfolio: bridges my SQL Server / ETL background into reusable Azure orchestration.

Production-oriented framework for metadata-driven backup scheduling, restore-chain planning, point-in-time recovery, marked-transaction rollback, canary validation, and execution telemetry.

Role in the portfolio: represents the reliability and recoverability mindset that also runs through my Azure work.

Governance POC using Microsoft Purview, ADLS Gen2, Azure Data Factory, Managed Identity, RBAC, classification, stewardship, business glossary metadata, automated lineage, and metadata persistence validation.

Role in the portfolio: demonstrates that trusted data also requires discoverability, ownership, classification, and lineage.

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 Hubs, Azure Functions, ADLS Gen2, progressive validation, current-state modeling, and Gold aggregation.

• 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.
• Lakehouse and analytical serving patterns.
• Data governance and lineage.
• Clear technical documentation and operational ownership.

The planned Azure technical portfolio is now complete across its core capability blocks. My current focus is positioning that work clearly for recruiters and technical interviews while continuing to deepen the underlying engineering skills.

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