Caleb Ochs Logo Caleb Ochs

Data Platform, Modeling & Power BI Consulting

Fix scattered systems, messy models, and slow reports.

I design and build analytics platforms from source-system integration through semantic modeling and executive reporting. Then I help teams operate them reliably in production.

Schedule a 25-minute call See how I help

Live diagnostic

Pick a scenario below and the system view shifts to show the path from scattered source systems to usable reporting.

10+ years

building data systems

Nearly 300

project engagements

50+

client organizations

End to end

source systems through BI

Finance, operations, and executive reports do not agree.
Source systems, models, reports, and owners have sprawled.
Business logic lives in too many reports, exports, and one-off fixes.
You need senior architecture and hands-on implementation.

Services

From source systems to trusted decisions.

The best analytics work starts below the dashboard. I help teams connect source systems, clean up business logic, design usable models, and deliver reporting people can actually trust.

Data platform & pipeline buildout

Turn scattered source systems into a reliable analytics foundation.

For teams dealing with disconnected systems, brittle refreshes, manual exports, slow pipelines, or unclear ownership between source data and reporting.

  • Fabric, Azure SQL, Synapse, Data Factory, ADLS, Lakehouse, and Warehouse architecture
  • SQL-based transformation layers and repeatable data loading patterns
  • Incremental refresh, historical tracking, and pipeline reliability
  • Practical architecture that your team can maintain after the project
Talk about your data platform

Semantic modeling & metric design

Build models that make the business numbers easier to trust.

For teams where reports technically work, but definitions are inconsistent, relationships are messy, and every meeting turns into "why doesn't this number match?"

  • Star schema and dimensional model design
  • Power BI semantic models and reusable measures
  • Finance-aligned KPIs and business definitions
  • Row-level security, performance tuning, and governance patterns
Talk about modeling and metrics

Power BI rescue & reporting modernization

Fix the reporting environment you already have.

For teams dealing with slow reports, dataset sprawl, duplicated metrics, messy workspaces, licensing confusion, or legacy reports that need to move without carrying the mess forward.

  • Report and model performance tuning
  • Workspace, dataset, and governance cleanup
  • Migration from Qlik, Tableau, SSRS, Excel, or legacy reporting tools
  • Executive dashboards and operational reporting that people actually use
Talk about Power BI rescue

Fabric, Azure & AI-ready analytics

Prepare your analytics stack for what comes next.

For teams moving into Microsoft Fabric, evaluating architecture choices, or trying to make Copilot and AI summaries useful instead of vague.

  • Fabric readiness and architecture planning
  • Lakehouse and Warehouse modeling patterns
  • Copilot-friendly semantic models, measure names, descriptions, and report context
  • Clear tradeoffs between speed, scale, cost, and maintainability
Talk about Fabric or AI readiness

Engagement preview

Semantic modeling sprint

Clean up relationships, business definitions, reusable measures, and security patterns so reporting starts from trusted logic.

01

Audit

02

Model

03

Govern

04

Launch

05

Summarize

06

Operate

07

Improve

Business and technical depth

The tools matter. Understanding the numbers matters more.

I work across the platform and the business logic inside it. That is especially useful when a data problem crosses finance, operations, source systems, models, and the reports leadership already uses.

Financial analytics

Revenue, margin, budgets, forecasts, WIP, EAC, revenue recognition, project accounting, liabilities, utilization, and source-to-ledger reconciliation.

Operational analytics

Service performance, field operations, inventory, distribution, memberships, franchise metrics, sales pipelines, equipment, workforce activity, and customer behavior.

Platform and engineering

Microsoft Fabric, Azure SQL, Data Factory, Synapse, Lakehouse and Warehouse patterns, SQL, SSIS, APIs, incremental loading, dimensional models, monitoring, and production support.

Models and decisions

Power BI, DAX, semantic models, SSAS Tabular, paginated reports, row-level security, performance tuning, metric governance, executive reporting, and stakeholder enablement.

Fabric, Azure & AI-ready analytics

Power BI is the visible layer. The model underneath is where trust is built.

Fabric, Azure, Power BI, and Copilot work best when the platform, model, and business logic underneath them are clean. That means reliable pipelines, sensible architecture, clear semantic models, and reports that describe the business question they answer.

I help teams make practical architecture choices and prepare analytics models so leaders can trust the reporting today and get more useful AI summaries tomorrow.

Analytics readiness

Built for teams. Structured for AI.

Practical architecture

Fabric, Azure, Lakehouse, Warehouse, and SQL choices matched to the team's reality.

Clean semantic models

Star schemas, plain column names, clear measure names, descriptions, and logical folders.

Trusted business logic

Measures that express one business idea cleanly instead of hiding definitions everywhere.

Reports with context

Pages, visuals, titles, and descriptions that tell Copilot what the report is meant to answer.

Example executive prompt

"Summarize margin performance by region, call out unusual movement, and explain which drivers changed most."

That only works well when the model already knows what margin, region, drivers, time periods, and report context mean.

Selected work

Complex underneath. Clear where it counts.

Representative engagements, anonymized for client confidentiality. Each one required architecture, hands-on engineering, business definition work, and long-term production ownership.

Fabric and project finance

Recent work

Unifying project and financial data across a growing enterprise.

Acquisitions and system migrations left project, agreement, sales, and financial reporting spread across D365 and multiple legacy operating systems with different definitions and histories.

What I owned

Fabric ingestion and medallion-layer patterns, D365 discovery, Synapse Link integration, transformation logic, semantic models, orchestration, validation, and stakeholder delivery.

What changed

Project budgets, WIP, EAC, revenue recognition, agreements, bookings, and sales-pipeline analysis began moving onto shared, refreshable models with explicit reconciliation logic.

Microsoft Fabric D365 Synapse Link Bronze / Gold SQL Power BI

Franchise analytics

One reporting language for hundreds of locations.

A growing franchise network needed to move beyond legacy Qlik reporting while keeping finance, operations, owners, and corporate leadership aligned on the same definitions.

What I owned

Migration strategy, warehouse and semantic-model changes, DAX, report modernization, reconciliation, row-level access, release support, and ongoing platform troubleshooting.

What changed

Finance-aligned metrics became reusable across executive, franchise, marketing, membership, and operational reporting instead of being rebuilt report by report.

Azure SQL Power BI DAX Qlik migration Semantic models

Service operations

A unified view across disconnected operating systems.

Field-service data lived across ERP, fleet, workforce, equipment, and legacy systems, making it difficult to understand performance, customer activity, and profitability together.

What I owned

Warehouse architecture, multi-system ETL, dimensional and tabular models, historical loading, production deployments, validation, monitoring, and years of operational support.

What changed

Leaders gained a common analytics foundation for service activity, technician performance, equipment, customers, fleet behavior, and operational profitability.

SAP IFS Fleet APIs SSIS SSAS Tabular Power BI

End-to-end foundation

A data warehouse built from the source systems up.

A multi-line operating business depended on separate vending, food-service, finance, inventory, and distribution systems with substantial manual reconciliation.

What I owned

Discovery, source-to-target mapping, Azure architecture, API and database ingestion, SSIS and Data Factory pipelines, dimensional models, tabular models, reporting, and documentation.

What changed

Sales, cost, inventory, distribution, and operational reporting could be reconciled to source systems and analyzed together from a maintainable analytics platform.

Azure Data Factory Azure SQL SSIS SSAS APIs Power BI

The same pattern, across very different businesses.

Additional work spans manufacturing, distribution, healthcare, housing, wine, professional services, project accounting, revenue recognition, and executive financial reporting.

Discuss a similar problem →

Process

A simple engagement path, not a consulting maze.

01. Diagnose

Clarify the real problem.

We review the current stack, reports, stakeholders, pain points, and what a better state needs to accomplish.

02. Design

Choose the practical path.

I map the target architecture, data model, governance approach, and build plan with clear tradeoffs and priorities.

03. Build

Implement with the team.

We build the models, reports, pipelines, and operating patterns so the result is useful, maintainable, and understood.

Caleb Ochs

About Caleb

Senior data help without the heavyweight consulting feel.

I am an independent consultant and former CTO who has spent the last decade working as a data engineer, architect, and analytics leader across nearly 300 project engagements and 50+ client organizations.

As CTO, I led through four departments with more than 20 indirect reports. That experience shapes how I work today: architecture has to fit the people who will build, support, govern, and use it.

I started in hands-on BI implementation with SQL, ETL, SSRS, tabular models, and Power BI, then grew into designing and owning complete analytics environments. I am comfortable in the weeds and equally comfortable explaining to executives why a metric behaves the way it does.

Most clients do not arrive with a perfect spec. They arrive with a stack that is too slow, too scattered, or too hard to trust. That is a good place to start.