Selected work

Summary

Designed and implemented a full end-to-end analytics pipeline covering data extraction, transformation, semantic modeling, and reporting.

Context

Multiple external providers delivered sales-out data through APIs and cloud data platforms that needed to be integrated into a single analytics layer with predictable behavior and acceptable performance. 14 source systems, 1B+ rows processed over 4 months.

Solution

Built a modular analytics pipeline spanning API connectivity, source system integration, upstream transformation, Power BI dataset optimization, and report delivery.

Outcome

Reliable and scalable analytics delivery with controlled data processing, optimized datasets, and consistent business reporting across 14 integrated sources.

Summary

Re-architected enterprise Power BI semantic models to improve performance, reuse, and metric consistency across reports.

Context

Legacy datasets and upstream pipelines evolved organically, resulting in slow refreshes (up to 30 min), duplicated logic, and sensitivity to schema changes across 20+ downstream reports.

Solution

Redesigned semantic models and optimized upstream data extraction using usage-driven column reduction and controlled ingestion.

Outcome

Dataset refresh cut from 30 min to 5 min, reduced operational errors, and a stable semantic layer serving 20+ reports.

Summary

Redesigned the Power BI deployment architecture to eliminate static DEV/TEST environments and enable Git-driven, ephemeral workspaces.

Context

Scaling Power BI delivery required removing manual promotion steps, simplifying environment topology, and enabling production changes without impacting end users. The platform needed to support up to 15 concurrent workspaces.

Solution

Introduced a production-only workspace model with branch-scoped ephemeral environments, fully automated via GitHub Actions.

Outcome

Simplified deployments supporting up to 15 concurrent ephemeral workspaces, reduced operational overhead, and zero-downtime production changes.

Summary

Automated deployments, regression tests, and approvals for Tabular models and Power BI reports.

Context

Power BI CI/CD pipelines originally implemented in Azure DevOps were migrated to GitHub Actions to align with a GitHub-first engineering platform. The platform handles ~30 deployments per month.

Solution

Designed and rebuilt the CI/CD architecture from scratch, introducing PR-level validation, regression checks, approval gates, and automated deployments for both semantic models and reports.

Outcome

~30 automated deployments per month with readable error traces, standardized release workflows, and reduced risk across environments.