Pipelines and ETL
Custom ingestion and transformation built for the enterprise systems you already run — ERP, CRM, finance, clinical, and operational sources — with orchestration, retries, and lineage rather than fragile scheduled scripts.
Data Engineering
Enterprise data engineering consulting
We build the backbone of enterprise data: clean pipelines, automated workflows, and scalable architecture across Microsoft Fabric, Snowflake, Azure, AWS, and Databricks. Independent, senior-only, and measured against your KPIs — not a vendor's roadmap.
- Delivery model
- 90-day cycles, embedded with your team
- Commitment
- Measurable ROI inside 90 days
- Independence
- No vendor kickbacks, no reseller margins
What we build
Warehouses and lakehouses
Dimensional models and lakehouse architecture on Microsoft Fabric, Snowflake, and Databricks, designed so analytics, ML, and reporting read the same governed definitions.
Real-time streaming
Event-driven pipelines for operational data that can't wait for an overnight batch, with backpressure and replay handled as first-class concerns.
Data quality at the source
Contracts, validation, and monitoring enforced where data is produced, so leadership stops reconciling three versions of the same number.
Latency and cost reduction
Query, storage, and compute tuning that cuts warehouse spend and shortens refresh windows — usually the fastest measurable win in the first 90 days.
Migration and modernization
Moving off legacy warehouses and hand-rolled integrations without freezing the business, sequenced by risk, value, and dependency.
Microsoft Fabric · Snowflake · Databricks · Azure · AWS · Terraform
Who we've done this for
Our consultants have delivered data and analytics engagements inside regulated, high-scrutiny environments — financial services, healthcare and medical devices, enterprise software, and higher education — where reporting has to withstand audit and downtime isn't an option.
- MicrosoftEnterprise software
- Federal Reserve Bank of RichmondFinancial services
- StrykerMedical devices
- DeloitteProfessional services
- IBMEnterprise technology
- Arvest BankBanking
- Moody'sRisk & analytics
- Northeastern UniversityHigher education
Current and past clients across the firm. References available on request.
What changes for the business
- Reporting that leadership trusts without a manual reconciliation step
- Analytics and AI initiatives unblocked because the data layer is finally reliable
- Lower cloud spend from right-sized storage, compute, and refresh schedules
- Internal teams able to extend the platform after the engagement ends
How we engage
- IListen
A 30-minute discovery call to map your stack, constraints, and highest-ROI opportunities.
- IIArchitect
A roadmap sequenced by business value, with build, buy, and skip decisions made in the open.
- IIIShip
Production pipelines and models delivered in 90-day cycles, measured against your KPIs.
- IVCompound
Handover and enablement so your team can extend the platform without us.
Common questions
What is enterprise data engineering consulting?
It is the design and delivery of the pipelines, warehouses, lakehouses, and data quality controls that make enterprise data reliable enough for reporting, analytics, and AI. We deliver that work in 90-day cycles, embedded with your team.
Which platforms do you work on?
Microsoft Fabric, Snowflake, Databricks, Azure, and AWS. Because we are 100% independent, platform recommendations follow your constraints rather than a partner quota.
How long does an engagement take?
Engagements run in 90-day cycles, with a standard commitment of measurable ROI inside the first cycle. Discovery is a free 30-minute call — and we tell you honestly when we are not the right fit.
Scope a data engineering engagement
Book a free 30-minute discovery call. We'll review your current stack, identify the highest-ROI opportunities, and tell you honestly whether we're the right fit.