Peter Mc Polin

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Lead Data Analyst · Analytics Platform Owner

Lead Data Analyst · Analytics Platform Owner

I build analytics platforms that make operational decisions easier to trust.

I build analytics platforms that make operational decisions easier to trust.

I build analytics platforms that make operational decisions easier to trust.

7+ years designing enterprise reporting ecosystems, scalable data pipelines and governance standards across engineering, finance, research, HR, talent acquisition, education and more.

7+ years designing enterprise reporting ecosystems, scalable data pipelines and governance standards across engineering, finance, research, HR, talent acquisition, education and more.

7+ years designing enterprise reporting ecosystems, scalable data pipelines and governance standards across engineering, finance, research, HR, talent acquisition, education and more.

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Portrait of Peter McPolin

Peter McPolin

Peter McPolin

Lead Data Analyst · Analytics Platform Owner

Lead Data Analyst · Analytics Platform Owner

SQL

Azure

Power BI

Python

500+

enterprise users

300+

repos analysed

  • 20+ data sources integrated

  • 20+ Azure pipelines built

  • 20+ APIs integrated

  • 50+ dashboards delivered

  • 4 workspaces managed

  • 4 external clients delivered

  • 4 industries delivered

  • 7+ years experience

Project Showcase

Project work and walkthroughs

Built work, not just claimed experience.

A practical look at some of the analytics products I’ve built: the data problem, the engineering route, the report experience and how teams use it day to day.

This section is ready for your completed project screenshots, videos and short write-ups.

GitHub analytics across 500+ repositories

Problem: the company had over 500 repositories, and pulling everything from GitHub in one connection was too slow and too unreliable for reporting.

Solution: I built API extraction into Azure, split branches, commits, comments, forks and related entities into separate tables, kept common IDs across the model, then used stored procedures to move the data into SQL for joining and querying before Power BI.

Outcome: product leaders now use it for progress reporting, standups, reviews, bottleneck analysis and a clearer view of engineering delivery.

Education client analytics product

Education client analytics product

Problem: industry data was spread across multiple government sources, with users needing one consistent place to understand it.

Solution: I built a polished Power BI product with a consistent left navigation, personalised greeting, clear filter states, AI visuals where useful, and alerts for quarterly data file updates that cannot be connected before they exist.

Outcome: the client gets a single, product-like reporting experience instead of chasing data across scattered public sources.

Outcome: the client gets a single, product-like reporting experience instead of chasing data across scattered public sources.

More examples: Research and Talent Acquisition

More examples: Research and Talent Acquisition

Research uses a different visual language, with blue styling, a custom calendar page, advanced tooltips and landing pages that echo the company slide deck. Talent Acquisition will show the same design consistency across another reporting area with amended data.

Research dashboard landing page screenshot
Power BI landing page screenshot
Talent Acquisition dashboard screenshot

This section shows range: different teams, different branding needs, same product-quality reporting standard.

This section shows range: different teams, different branding needs, same product-quality reporting standard.

How I Build Analytics Platforms

A platform is only useful when people can rely on it.

A platform is only useful when people can rely on it.

A platform is only useful when people can rely on it.

My approach combines pipeline engineering, modelling, governance, stakeholder delivery and reporting UX, the pieces that make analytics survive beyond the first dashboard.

My approach combines pipeline engineering, modelling, governance, stakeholder delivery and reporting UX, the pieces that make analytics survive beyond the first dashboard.

01

Map the operational system

Understand sources, owners, definitions, risks and the real decisions the platform must support.

02

Engineer reliable pipelines

Build API integrations, scheduled data flows, Azure lake patterns and source-controlled transformations.

03

Model for trust and governance

Define reusable semantic models, RLS, ownership rules, quality checks and reporting standards.

04

Ship decision-grade reporting

Design executive-ready Power BI experiences, adoption workflows and feedback loops that expose trends and anomalies.

Data protection and portfolio availability

To protect company data, names, project labels, source systems and selected values have been amended or anonymised where appropriate. The showcase demonstrates the type, quality and structure of the work without exposing confidential business information. More portfolio items, deeper walkthroughs and supporting artefacts are available to review on calls or during interviews.

Contact

Download the CVs or get in touch.

If you need analytics that survive contact with real operations, let’s talk.

If you need analytics that survive contact with real operations, let’s talk.

Download the PDF CV or a fun PBIX version directly. Additional artefacts and deeper walkthroughs can be shared on calls or interviews where appropriate.

mcpolin5@hotmail.com

07590 464559

Download PDF CV

Download PBIX CV

What I can evidence

• Enterprise reporting ecosystems across multiple functions

• API pipelines, Azure Data Lake patterns and Power BI delivery

• Governance, RLS, standards, stakeholder delivery and mentoring

© 2026 Peter Mc Polin · Lead Data Analyst · Analytics Platform Owner