DataOps & Lakehouse Engineering

The data foundation every AI product depends on

Great models start with trustworthy data. Our data engineering team designs the Lakehouse foundation that powers analytics and machine learning — unifying batch and streaming sources into governed, versioned tables that every team in your organization can rely on.

We apply DataOps practices end to end: traceable lineage, automated quality checks, and pipelines treated as production software — version-controlled, tested and orchestrated — so fresh, correct data is always ready for training and inference.

We build on Delta Lake and Apache Spark with Lakeflow declarative pipelines, Lakeflow Jobs and Unity Catalog on Databricks, and integrate with AWS, Kafka and your existing warehouse where needed.

Our data engineering services give your data and ML teams a foundation they can build on:

• Ingestion & Transformation - reliable batch and streaming ingestion with Auto Loader and Spark, transforming disparate sources into a single, analysis-ready format across bronze, silver and gold layers.
• Declarative Pipelines & Orchestration - production-grade pipelines built with Lakeflow declarative pipelines and orchestrated as Lakeflow Jobs, with dependencies, retries, conditional flows and event-driven triggers.
• Data Quality & Testing - automated schema validation, quality checks and data-dependency tests baked into the pipeline, so issues are caught early instead of in production.
• Governance & Lineage - centralized access control, discovery and real-time lineage with Unity Catalog, giving you one consistent, auditable view of your entire data estate.