Data Engineering — Turn sprawl into one source of truth.
Turn sprawl into one source of truth.
Reliable reporting. AI-ready data foundations. ETL / ELT that does not break on a Tuesday night. We ship fixed-fee Data Foundation Assessments that tell you whether you need Databricks, Snowflake, Microsoft Fabric, or a warehouse-plus-dbt starter — and we implement whichever answer fits your team, not our sales quota.
Why SMBs Stall on Data
Three patterns. One: reporting is unreliable — every dashboard tells a slightly different story and everyone has lost trust in the BI tool. Two: AI ambitions are blocked — a pilot stalled because "the data is not ready" and nobody owns the fix. Three: a platform decision is looming — Databricks, Snowflake, Microsoft Fabric, or a pure warehouse-plus-dbt stack — and there is no-one internally who can scope it independently. All three are data engineering problems. The answer is not buying a bigger platform. The answer is starting with an assessment that chooses the right one.
What We Build
| Offer | Outcome | Timeline | Commercial Model |
|---|---|---|---|
| Data Foundation Assessment | Decision-ready architecture, platform recommendation, and fixed-fee build plan | 2-3 weeks | Fixed fee |
| ETL / ELT Modernization | Fivetran / Airbyte / dbt ingestion layer replacing brittle scripts and manual exports | 6-10 weeks | Fixed fee |
| Databricks Lakehouse | Bronze / silver / gold medallion architecture on Databricks with unity catalog governance | 10-16 weeks | Milestone |
| Snowflake Analytics Foundation | Snowflake warehouse with dbt models, role-based access, and reporting layer | 8-12 weeks | Milestone |
| Microsoft Fabric | Unified Fabric deployment (OneLake, Warehouses, Data Factory, Power BI) | 10-14 weeks | Milestone |
| BI Acceleration | Power BI / Tableau / Looker / Zoho Analytics semantic layer and dashboards | 6-10 weeks | Fixed fee |
| Data Governance | Catalog, lineage, access, PII handling, and retention policy | 4-8 weeks | Fixed fee |
| AI-Ready Data Foundation | Curated, permissioned data surfaces ready for RAG and AI workloads | 8-12 weeks | Fixed fee |
| Analytics Managed Services | Ongoing pipeline operations, dashboard maintenance, and model refresh | Monthly | Monthly starting point |
Platform Expertise
We are certified partners and active practitioners across the modern data stack.
Medallion architecture, Unity Catalog, Delta Live Tables, MLflow
Role-based access control, dbt projects, Snowpark, semantic layer, Snowpipe for streaming
OneLake lakehouse, Warehouses, Data Factory, Power BI integration, Copilot in Fabric
dbt (Core and Cloud), AWS Glue, Step Functions, Airflow
Fivetran, Airbyte
Power BI, Tableau, Looker, Zoho Analytics
How Our Clients Find Us
Not sure where to start? Here are the five most common triggers that launch a data engineering engagement.
Ready to modernize your data stack?
Book a Data Foundation Assessment or explore how we've solved data sprawl for other SMBs.
Frequently Asked Questions — Data Engineering
Databricks vs Snowflake vs Microsoft Fabric — which should we pick?
It depends on your starting data estate, your team's platform familiarity, and whether AI / ML is a near-term requirement. Our Data Foundation Assessment answers this question in 2 to 3 weeks with an architecture, a platform pick, and a build plan.
Do we need a lakehouse, a warehouse, or both?
Most SMB estates start with a warehouse plus dbt. A lakehouse (Databricks, Fabric OneLake, Iceberg-on-S3) becomes the right answer when unstructured data, ML workloads, or cost pressure at scale enter the picture. We do not over-architect.
How much does a Data Foundation Assessment cost?
See the Packages page for the published fixed fee. The deliverable is a decision-ready architecture, a platform recommendation, and a fixed-fee build plan.
Can you work with our existing warehouse?
Yes. If you already run Snowflake, Databricks, Redshift, BigQuery, Synapse, or Postgres-as-warehouse, we extend and modernize rather than re-platforming for the sake of it.
What about data governance?
We implement Unity Catalog (Databricks), object tags and row-access policies (Snowflake), or Microsoft Purview (Fabric) depending on platform. Governance is scoped separately so it does not get cut under budget pressure.
Can you prepare data for AI workflows?
Yes. Our AI-Ready Data Foundation work cleans, permissions, and surfaces the specific data an AI workflow needs, with freshness, lineage, and access logging baked in.
Do you support streaming?
Yes. Kafka, Kinesis, Event Hubs, Snowpipe, and Delta Live Tables for streaming workloads where batch is not fast enough.
Which BI tools do you build on?
Power BI, Tableau, Looker, Zoho Analytics, Sigma, and Mode. We have a bias toward semantic layers in dbt so the BI layer is not the source of truth.
Is my data stored in the US?
Yes by default. We deploy on AWS us-east / us-west, Azure East US / Central US, and Snowflake / Databricks / Fabric US regions.
How do we start?
Book a Data Foundation Assessment. It is the most expensive thing to skip and the cheapest thing to buy.