Pipelines that run reliably, scale when needed, and don't wake anyone up at 3am. We build production-grade data infrastructure using modern tools and patterns that have been battle-tested across enterprise environments.
A data lakehouse combines the low-cost, flexible storage of a data lake with the structure, performance, and reliability of a data warehouse in one platform. It lets an organization run analytics, BI, and AI on a single governed copy of its data - without maintaining two separate systems.
Build reliable data pipelines using PySpark, Spark SQL, and Delta Lake. We design for idempotency, incremental processing, and graceful failure handling—because pipelines that "usually work" aren't good enough.
Design and implement orchestration patterns using Azure Data Factory, Databricks Workflows, or Airflow. We build dependency-aware scheduling, monitoring, and alerting that keeps data flowing on time.
Implement and optimize cloud data infrastructure on Azure and Databricks. We configure clusters, storage, networking, and security so your platform performs well and doesn't blow past budget.
Build streaming pipelines using Spark Structured Streaming, Event Hubs, and Kafka. We implement exactly-once semantics, watermarking, and windowing for use cases that can't wait for batch.
Implement data quality frameworks using Great Expectations, Delta Live Tables expectations, and custom validation. We build tests that catch problems before they reach dashboards and downstream systems.
Diagnose and fix slow pipelines. We tune Spark jobs, optimize Delta table layouts with Z-Ordering and partitioning, and right-size clusters to cut costs while improving performance.
Whether you're building from scratch, migrating legacy ETL, or trying to fix pipelines that keep breaking—let's talk about what reliable data engineering looks like for your organization.
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