Every engagement starts from the same question: what decision is this data supposed to support, and what would it cost to be wrong? The answer sets the scope. These are the four kinds of work I take on, directly or through Spectra Data Services, for mid-size and regulated businesses that need senior data capability without a forty-person platform team.
Business intelligence and reporting
Power BI semantic models built on a proper star schema, with calculation groups, field parameters, dynamic row-level security and reconciled measures. Paginated and SSRS reports for the statements, statutory filings and operational documents that have to print exactly right. Executive scorecards, drill-through operational pages and alerting on the metrics that matter.
- Semantic model design, DAX and performance tuning
- SSRS to Power BI paginated migration
- Balanced scorecards and KPI frameworks that tie to the ledger
Data platform and pipeline engineering
OLTP and OLAP modeling, SQL Server data warehouses, and the pipelines that feed them: SSIS, Azure Data Factory, Microsoft Fabric Data Factory, Prefect and dbt. Medallion lakehouse designs on OneLake for teams that are ready for Fabric, and honest advice about when they are not.
- Dimensional modeling and warehouse design
- ADF and Fabric pipelines, mirroring and Direct Lake
- Code-first orchestration with Prefect and dbt
Custom dashboards and AI-enabled analytics
When the packaged tool is the wrong shape for the job: customer-facing portals, embedded analytics and bespoke visualizations built with HTML5, CSS3, Node.js, Tailwind and D3, with Python for forecasting and scoring. Practical generative-AI adoption: Copilot readiness, semantic-layer design for data agents, and guardrails that keep answers accurate.
- Embedded and customer-facing dashboards
- Forecasting, scoring and what-if models in Python and R
- AI readiness assessments for BI estates
Data security, governance and fractional leadership
Row-level and object-level security, encryption and least privilege on SQL Server and Power BI. Lineage, quality tests and a governance cadence a two-person team can keep. Incident-response experience on production data platforms. Available as a fractional data lead for companies that need a senior owner of the data estate a few days a month.
- Data security and governance assessments
- Vendor selection and implementation oversight
- Fractional data and BI leadership
How an engagement runs
- Discovery (1–2 weeks). Interviews, system inventory, a written statement of the decisions the data must support, and a prioritized backlog with effort and risk.
- Build (fixed scope). Model, pipeline or report delivered with reconciliation evidence, documentation and a handover session. No black boxes.
- Run (optional retainer). Monthly reviews of refresh health, security, performance and the backlog, so the platform keeps earning its keep.
