The data foundation everything else runs on.
Pipelines, warehouses, and lakehouses that turn scattered sources into one governed, reliable layer.
Overview
Analytics and AI are only as good as the data beneath them.
Most teams lose weeks to brittle exports, metrics that disagree, and pipelines no one trusts — and every new dashboard inherits the mess.
We build the ingestion, modeling, and governance layer that makes everything downstream faster to build and safe to trust.
What we deliver
Real-time & batch pipelines
Ingest from any source — databases, APIs, events, files — on the cadence each use case needs, without hand-built glue.
Warehouse & lakehouse modeling
Well-modeled data in Snowflake, BigQuery, or Databricks that stays fast and affordable as it grows.
Identity resolution & unification
Stitch fragmented records into one clean entity, so your metrics finally agree.
Data quality & observability
Automated tests, freshness checks, and alerting catch bad data before it reaches a dashboard.
Governance, lineage & access
Know where every number came from and who can see it — audit-ready by default.
Legacy migration & cost control
Move off aging stacks with parity checks, and tune spend so the platform pays for itself.
How we work
Governance-first. Security, lineage, quality, and access control go in from day one — and we meet your stack where it is instead of forcing a rebuild.
Discover & map
We inventory every source, owner, and definition — and agree what each metric actually means — before writing a line of pipeline.
Model for use
We design the warehouse or lakehouse around the questions you'll actually ask, not the shape of the source systems.
Build with guardrails
Pipelines ship with automated tests, lineage, and access control from the first commit — quality is not a later phase.
Run & hand over
Monitored, documented, and cost-tuned — owned by your team with full knowledge transfer, or run by ours.
Tech we use
A pragmatic, best-of-breed stack — chosen per project, not by default.
Ingestion & CDC
- Airbyte
- Fivetran
- Debezium
- Kafka Connect
- REST / GraphQL APIs
Streaming
- Apache Kafka
- Spark Structured Streaming
- AWS Kinesis
- Google Pub/Sub
Warehouse & Lakehouse
- Snowflake
- BigQuery
- Databricks · Delta Lake
- Amazon Redshift
- Apache Iceberg
Storage & Databases
- PostgreSQL
- MongoDB
- Amazon S3
- Google Cloud Storage
- Azure Data Lake
Transformation & Modeling
- dbt
- Apache Spark
- SQL
- Python
- pandas
Orchestration
- Apache Airflow
- Dagster
- Prefect
Governance & Quality
- Unity Catalog
- Great Expectations
- OpenLineage
- Data contracts
Cloud & Infra
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Terraform
Results
Case studies powered by Data Engineering.
Industries we apply this in
Let's put Data Engineering to work.
Tell us what you're trying to move — we'll show you how AI and data get you there.