Data Engineering & Integration

Connect business systems and build reliable data foundations for analytics, applications, and AI.

Sources disagree, pipelines fail quietly, and teams rebuild the same extracts every month.

We design and build integrations, ETL/ELT pipelines, warehouses and lakehouses, quality checks, and observability—so refreshes meet schedule, failures are visible, and access is appropriate.

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How this work looks

Engineering builds the lakehouse and pipelines behind Connect—sources in, a trusted foundation out.

Connect → Understand → Apply AI → Act
Interactive demo · Sample data · Simulated AI
1/4

Step 4 · Shared semantic model

Shared semantic model

One dictionary for dashboards, AI, and workflows

Revenue

$1.25M

Open Pipeline

$420k

Conversion Rate

28%

  • Revenue
  • Open Pipeline
  • Conversion Rate

Ready for interactive analytics →

Connected sources. One trusted data foundation.

What’s included

Data Stack Implementation

Stand up the integration, warehouse/lakehouse, and operating pieces your analytics and AI workloads need.

Data Integration & Pipelines

ETL/ELT and source integrations with least-privilege credentials and clear contracts—not one-off notebook scripts.

Data Warehousing & Lakehouses

Curated layers designed for analytics and AI, including Fabric and Databricks lakehouse patterns where they fit.

Data Quality & Governance

Validation rules, reconciliation, refresh visibility, and ownership so broken loads do not silently reach leadership.

Platform migration & modernization

Move or consolidate pipelines with documented lineage and rollback thinking.

Orchestration, refresh management & documentation

Schedules, runbooks, and technical docs so the client knows who owns ongoing operation.

Example use cases

Hypothetical patterns · Not customer claims

CRM + finance reconciliation

Join pipeline and billing signals so revenue metrics match agreed source totals.

Fabric lakehouse foundation

Ingest, curate, and document datasets that Power BI and AI workloads can share.

Pipeline observability

Make refresh failures and stale loads visible before month-end.

What engagement includes

  • Source and access discovery with least privilege
  • Deployed pipelines and curated datasets
  • Quality checks and reconciliation evidence
  • Schema/contract documentation and runbooks
  • Handoff of ownership and operating expectations

What we ask from you

  • System access or coordinated extracts
  • Business owners for reconciliation rules
  • Refresh and availability requirements

Sample deliverables

  • Deployed pipelines
  • Curated datasets
  • Data contracts / schema docs
  • Quality checks
  • Lineage where supported
  • Operating runbooks

Engagement boundaries

  • Not general IT support or 24/7 infrastructure monitoring.
  • Secure credentials for scoped integrations are in scope; enterprise-wide identity administration is not.

Relevant platforms: Microsoft Fabric · Power BI · Palantir Foundry

Technologies we work with

Confirmed capabilities only — not certifications or partnership badges.

  • Power BI
  • Microsoft Fabric
  • Palantir Foundry
  • SQL
  • dbt
  • Python
  • Power Apps
  • Power Automate
  • Azure Data services
  • Databricks

FAQs

No. We provision and operate data workloads in scope. General network, server, and tenant administration belong elsewhere.

Ready to talk through this capability?

Tell us about your situation. We will help you define a practical next step.

Discuss Your Project