Service
Lakehouse & Pipeline Engineering
Clean, reliable data flows from every source your team depends on - with the quality gates and observability to trust it.
The Problem
Most industrial analytics failures aren't model failures - they're pipeline failures. Data arrives late, schema changes silently break transforms, and no one owns the fix. Building pipelines that are observable, testable, and maintainable requires engineering discipline that most teams learn the hard way.
What You Get
- ▸End-to-end batch and streaming pipeline design and implementation
- ▸Lakehouse architecture (medallion layers, Delta tables, Unity Catalog)
- ▸Data quality checks and schema drift detection
- ▸Orchestration with scheduling, retries, alerting, and lineage
- ▸Pipeline documentation and data lineage map
Stack & Tools
Databricks, Delta Lake, Delta Live Tables, Unity Catalog, Auto Loader, Apache Spark, Apache Kafka, Apache Airflow, dbt, Great Expectations, Python, SQL.
How We Work
Phase 1
Discovery
Map all data sources, consumers, and existing transforms. Identify quality issues, latency requirements, ownership gaps, and the current cost of bad data.
Phase 2
Design
Define the pipeline architecture - batch vs. streaming, medallion layers, orchestration tooling, testing strategy, and the target Unity Catalog structure.
Phase 3
Build
Implement pipelines with quality gates, deploy orchestration, and validate end-to-end with production or production-representative data.
Phase 4
Enablement
Train your team on pipeline operations, hand off alerting ownership, and document the patterns so your engineers can extend them independently.
Right for You If…
- ✓You have multiple data sources that need to land in one place for analysis
- ✓Your current pipelines are fragile - schema changes or source outages cause silent failures
- ✓You're spending engineering time fixing data rather than using it
- ✓You're adopting a lakehouse and need to build the ingestion and transformation layer
What You'll Need to Bring
- ▸Defined source systems and access credentials or a clear path to obtain them
- ▸At least one known downstream consumer - a report, model, or dashboard
- ▸Willingness to participate in data quality reviews during the build phase
Ready to get started?
Tell us where you are and what you're trying to solve. We'll let you know if we're the right fit.
Schedule a Consultation