Data Engineering & Modern Data Platforms
Modern lakehouses, automated ETL/ELT pipelines, Snowflake, Databricks, and high-performance data lakes at scale.
The platform everything else depends on.
Reporting that disagrees between teams, pipelines that fail silently overnight, a warehouse bill that grows faster than the business, these are architecture problems, not tooling problems.
We design and build data platforms as software: modelled deliberately, version controlled, tested in CI, and observable in production. The result is a single set of numbers the business agrees on, and pipelines your team can change without fear.
It is also what makes AI viable. Retrieval quality, model accuracy and agent reliability are all downstream of the data layer.
Typical engagements
- Greenfield lakehouse on Databricks or Snowflake
- Migration off legacy SSIS or Informatica ETL
- Near real-time ingestion to replace nightly batch
- Warehouse cost and performance optimization
- Data quality and governance remediation
What we build.
Lakehouse architecture
Bronze/silver/gold layering on Delta or Iceberg, with schema evolution and time travel handled properly.
ETL / ELT pipelines
Orchestrated batch and streaming ingestion with retries, backfills and idempotent loads.
Warehouse modeling
Dimensional and data-vault models in Snowflake, Synapse or BigQuery, documented and tested with dbt.
Legacy ETL migration
Re-platforming SSIS, Informatica and hand-rolled scripts, with parallel-run validation before cutover.
Quality & observability
Contract tests, freshness and volume monitors, column-level lineage and alerting that reaches a human.
Analytics enablement
Semantic layers and certified datasets so Power BI and Tableau users stop rebuilding the same logic.
Tools chosen for your constraints, not our habits.
We work across the major cloud data stacks and will tell you plainly when the cheaper option is good enough.
Snowflake
DatabricksAzure Data FactoryMicrosoft Fabric
dbt
Apache Spark
Apache Kafka
Airflow
Delta LakeApache IcebergPower BI
Terraform
Three ways to start.
Platform assessment
A review of your current pipelines, models and costs, with a prioritized remediation plan.
Build or migrate
End-to-end delivery of a new platform or a migration off legacy tooling, with parallel-run validation.
Run and support
SLA-backed operation of your pipelines and platform, including on-call and continuous optimization.
Common questions.
Snowflake or Databricks?
It depends on your workload mix, existing skills and cloud commitments. If most of the work is SQL analytics, Snowflake is usually simpler to run; if you have heavy Spark, ML or streaming needs, Databricks earns its complexity. The assessment answers this for your case rather than in the abstract.
Can you migrate us without downtime?
Standard practice is a parallel run: the new pipelines populate the new platform alongside the old one, outputs are reconciled row by row, and cutover happens only once the numbers match for an agreed period.
Will our team be able to maintain it?
That is the point. Everything is version controlled with documented conventions, and handover including pairing sessions and runbooks is part of the engagement, not an afterthought.
Can you reduce our existing cloud data bill?
Often, yes, through warehouse sizing, partitioning and clustering changes, query rewrites and storage tiering. We start by measuring where the spend actually goes before changing anything.
Have a project in mind?
Tell us about your project and a member of our engineering team will get back to you within one business day.