I build pipelines other people can trust.
Data & Infrastructure Engineer building pipelines that move fast and think for themselves. From cloud-native ETL on Azure to AI-assisted monitoring that explains itself. Azure native data platforms: Ingestions, orchestration, warehouse cost control, and the monitoring layer that catches problems before they reach the dashboard.
I am a Data Engineer with a background in IT infrastructure, cloud engineering, and network engineering. I specialize in building scalable cloud-based data platforms using Azure Data Factory, Databricks, Python, and SQL. My focus is on designing reliable data pipelines that transform raw data into trusted business insights.
I came into data engineering through IT infrastructure, networking, and cloud engineering — which means I think about data pipelines the way I think about networks: what happens when a link drops, where the bottleneck actually is, and who gets paged.
Today, I build cloud data platforms on Azure. Data Factory for orchestration, Databricks and PySpark for transformation, Data Lake for zoned storage, SQL for the modelling layer. The goal is always the same: turn raw, inconsistent source data into datasets people are willing to make decisions on.
The infrastructure background still does work. I have built and run three-tier applications on AWS, designed VPC networking with private endpoints, and handled snapshot and AMI disaster recovery. Most data engineers can write the transformation; fewer can tell you why the subnet routing broke it.
I am also working on where AI genuinely helps a data workflow — quality monitoring, documentation, anomaly explanation — with a firm line: models explain and draft; tests decide.
Migrated a batch-based ETL workflow to a cloud-native, event-driven pipeline on Azure — cutting processing time from 5 hours down to 65 minutes. Airflow handled orchestration; Data Lake Storage managed raw-to-curated data zoning.
Built an automated data quality layer that uses an LLM to flag anomalies and generate plain-language summaries of pipeline failures for non-technical stakeholders — cutting time-to-diagnosis by 80%.
Designed a streaming pipeline ingesting live transaction data via Kafka, feeding a real-time dashboard the business team uses to track transaction trends and support day-to-day decisions.
Refactored a growing Snowflake warehouse — partitioning, query tuning, and workload isolation — reducing monthly compute cost by 90% while improving query times for the analytics team.