Commercial Analytics Leader Job at Megaport, Vermont

  • Megaport
  • Vermont

Job Description

About Megaport

We're not your typical tech company - and we don't want to be. Megaport is the global leader in Network as a Service (NaaS), and has transformed the way businesses connect to the cloud, data centers, and each other. We're publicly listed on the Australian Stock Exchange and partnered with the biggest names in tech like Amazon, Microsoft, Google, Oracle, IBM, and more. Headquartered in Brisbane with a crew of over 600 people spread across Asia-Pacific, Europe, and the Americas, our employees enjoy an environment that is collaborative, supportive, and (actually) fun.

Our Team Culture

We're a team of problem solvers, pixel pushers, code slingers, and cloud fanatics. Culture is more than a poster on the wall here - collaboration beats hierarchy, curiosity fuels our growth, and everyone's voice matters. We take our work seriously, but not ourselves. We work across time zones to execute on our global vision, trust each other to get things done, and never compromise our values for commercial gain. Most importantly, we place our customers at the center of everything we do.

We're committed to increasing representation in the tech industry and welcome applicants from all backgrounds. Don't meet every requirement? That's okay. If you're excited about this role, we encourage you to apply.

The Role

The Commercial Analytics Leader serves as the primary strategic interface between commercial leadership and the organization's data, analytics, and data-science capabilities. This role connects trusted data, technical analytics, and business strategy to accelerate execution. The Leader makes accurate commercial intelligence continuously available, understandable, and actionable across core and expansion sales motions.

Focused on enhancing sales performance, revenue reporting, and commission-attainment metrics, the role will progressively expand into predictive analytics, commercial optimization, AI-powered intelligence, and governed self-service capabilities. This role will move the business beyond static reports and dashboards with an on-demand commercial intelligence engine that connects data and insights to recommendations for better decisions and measurable business outcomes.

The ideal candidate brings a builder's mindset to establish trust and reliability, standardization and scale, and continuously improve how the organization discovers and acts on commercial intelligence. Combining commercial leadership, analytical rigor, technical fluency, and the ability to influence decisions across organizational boundaries, this person translates sophisticated analytical outputs into clear business implications and practical recommendations.

What You'll Be Doing

Commercial Intelligence Strategy and Roadmap

  • Define and own the commercial analytics strategy, roadmap, and operating standards across brands, products, regions, and commercial motions.

  • Translate executive priorities and commercial objectives into a prioritized portfolio of analytical capabilities, business questions, and measurable outcomes.

  • Establish the progression from reliable descriptive performance reporting to diagnostic, predictive, and prescriptive AI-enabled decision intelligence.

2. Commercial Performance Metrics and Reporting Architecture

  • Own the commercial architecture and governance of RevOps performance reporting, including metric definitions, calculation rules, analytical views, reporting standards, and management cadences.

  • Build a coherent reporting framework based in Salesforce, collaborating with Power BI, billing sources and finance systems, marketing systems, and other relevant commercial data tools.

  • Reduce duplicated calculations, conflicting reports, spreadsheet dependencies, and legacy dashboards through standardized, reusable analytical assets.

3. Data Integrity, Reliability, and Analytical Trust

  • Partner with RevTech, Data Engineering, Data Science, Finance, and source-system owners to validate analytical logic, reconcile discrepancies, identify data gaps, and resolve recurring quality issues.

  • Establish appropriate standards for data freshness, reconciliation, completeness, lineage, calculation validation, change management, and issue escalation.

  • Make analytical reliability, traceability, and business confidence measurable operating priorities.

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