Analyst, Platform & Enablement

McAfee•Waterloo, ON

2d•CA$122,010 - CA$165,070•Hybrid

About The Position

We are seeking a Data Analyst to join the Analytics Architecture team within McAfee’s Analytics Platform & Enablement organization. In this role you will build, validate, and maintain the analytical logic behind McAfee’s most critical consumer business metrics — retention rate, trial conversion, bookings, subscriber counts, and marketing effectiveness. You will work directly in Databricks writing SQL and Python to transform raw data into certified KPIs that drive decisions at the VP and C-level. This is a hands-on, high-output role. You will own end-to-end analysis cycles: receiving business questions from leadership, translating them into data logic, validating results against source-of-truth systems, and delivering findings that directly influence how McAfee measures and optimizes its subscription business. You will partner closely with Data Engineering, Finance, Marketing, and Product teams on a daily basis. This position reports to the Senior Manager of Analytics Architecture and works alongside 2–3 peer analysts who collectively own the analytical foundations for McAfee’s consumer business. This is a Hybrid Position located in either Waterloo or Toronto, Canada. We are only considering candidates within a commutable distance to either location. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office.

Requirements

5+ years of experience in data analytics, business intelligence, or a related quantitative field.
Deep expertise in subscription or SaaS business metrics — including retention rate, churn, auto-renewal, trial conversion, LTV, and bookings — ideally in a consumer technology or eCommerce context.
Data and coding fluency — comfortable using SQL, Python, and generative AI tools to access data and deliver analysis independently.
Hands-on experience with Databricks (SQL, Python, PySpark, Delta Lake) and can write, review, and troubleshoot complex analytical queries and data pipelines.
Experience with BI and reporting platforms — particularly Power BI.
Experience with data validation and reconciliation — comparing data across source systems, identifying discrepancies, and documenting business rules that govern metric calculations.
Comfortable performing root cause analysis — diagnosing why a metric moved, tracing issues through multiple data layers, and presenting findings clearly to both technical and non-technical stakeholders.
Ability to translate ambiguous business questions into structured analytical approaches — scoping the data needed, defining assumptions, and delivering concise answers under time pressure.
Experience working in agile environments — managing your own backlog across 2-week sprints, balancing recurring reporting with project work, and communicating progress and blockers proactively.

Responsibilities

Build and validate the data logic behind core business KPIs — translating business definitions into certified analytical models across multiple dimensions.
Perform end-to-end data reconciliation — validating analytical datasets against finance and operational source-of-truth systems, investigating discrepancies, and ensuring accuracy of reported metrics.
Conduct customer lifecycle analysis — defining and measuring customer populations, segmenting behavior across acquisition, engagement, and renewal stages, and identifying drivers of key business outcomes.
Build waterfall and funnel analyses — decomposing aggregate metric movements into contributing factors, quantifying the impact of each, and presenting findings to leadership in a clear and actionable format.
Build and maintain data models and transformation logic — working in Databricks to create, migrate, and backfill analytical tables, validate data transformations, and ensure consistency between raw and curated data layers.
Produce and maintain automated dashboards and reports — building executive-facing visualizations that track business performance across multiple data sources with automated refresh pipelines.
Conduct root cause analysis on metric anomalies — investigating unexpected movements in KPIs, tracing issues through multiple data layers, and producing actionable findings for business stakeholders.
Deliver ad-hoc analysis for senior leadership — responding to executive requests with data-driven insights on topics such as customer sizing, audience segmentation, and business performance deep dives.
Document data lineage and business logic — mapping how metrics flow from source systems through transformation layers to final reporting, ensuring traceability and auditability of all certified KPIs.
Collaborate cross-functionally with Data Engineering, Finance, and Data Stewards to certify data logic before it enters production reporting, and flag data quality issues proactively.