Cloud platforms
Amazon Web Services (AWS) / Microsoft Azure
Cloud storage, compute and integration foundations: Amazon S3 and AWS workers, alongside ADLS and Azure Data Factory.
Expertise
Depth across the data platform.
Cloud engineering across AWS and Microsoft Azure. Data platform expertise in Databricks and Snowflake. We connect the layers that turn source systems into reliable analytics and application data.
Data platforms
Databricks / Snowflake
Lakehouse and warehouse engineering, from raw ingestion to dbt transformations, governed analytics and application integration.
Ingestion
Route batch, file and event sources through reusable utility suites. Simple YAML definitions select the appropriate ingestion code, source settings and target tables.
Processing
Build distributed processing workloads and focused workers, with compute choices shaped by data volume, execution time and operational requirements.
Storage
Structure durable landing zones and platform tables around clear ownership, lifecycle management and consumption needs.
Transformation
Turn source data into tested, documented models that downstream teams can understand and use.
Orchestration
Coordinate workloads with observable dependencies, safe retries and practical recovery procedures.
Governance & Reliability
Make ownership, access controls, monitoring and production engineering part of the platform from the start.
Serving & Integration
Serve curated models to BI and analytics, or use AWS workers and Lambda functions to query Snowflake and push controlled data payloads to downstream APIs.
Engineering judgment first
A stack is a set of decisions.
We make the trade-offs explicit.
Batch or streaming. Lakehouse or warehouse. Managed services or custom components. We evaluate each choice against your data, team capabilities, latency requirements and operational constraints.
A clearer way forward
Your data platform should accelerate the business,
not constrain it.
Let’s discuss what you’re building.