Senior Data Analyst (Remote)

The Senior Data Analyst will help build trusted Finance data products and expand self-service analytics across FP&A. Working within the enterprise architecture and standards established by Shared Data Services, this role will translate Finance needs into scalable data solutions, validate and operationalize delivered assets, and directly build data models, transformations, pipelines, and dashboards when needed.

This is a highly technical individual-contributor role combining hands-on SQL and Python development, advanced analytics, data quality, business intelligence, and stakeholder partnership. The role will help Finance move from manual and fragmented analysis toward reusable data products that support driver-based forecasting, portfolio performance analysis, ad hoc decision support, and executive reporting.

Primary Responsibilities

Build Trusted Finance Data Products

• Build, enhance, and maintain Finance tables, views, transformations, pipelines, and analytical models within established enterprise architecture, security, development, and governance standards.

• Develop reusable data products supporting bookings, customers, billing, revenue, operational activity, and portfolio performance.

• Use SQL and Python to automate data preparation, validation, and analytical workflows, and convert repeatable analyses into scalable assets.

• Apply appropriate source control, testing, deployment, monitoring, and documentation practices to Finance data products.

Partner, Validate, and Operationalize

• Translate FP&A and Portfolio Analytics needs into clear source requirements, data grain, business rules, transformation logic, quality controls, and acceptance criteria.

• Own Finance requirements, prioritization, validation, and acceptance for data products delivered in partnership with onshore and offshore Shared Data Services teams.

• Reconcile outputs to source systems, financial reporting, operational reporting, and approved methodologies; identify and resolve data quality issues, logic gaps, hierarchy problems, and incomplete coverage.

• Maintain documentation for metric definitions, data lineage, business rules, assumptions, and known limitations, and coordinate issue resolution through Finance sign-off.

Deliver Advanced Analytics and Self-Service

• Use SQL, Python, and appropriate analytical methods to solve complex customer, revenue, pricing, forecasting, portfolio, and operational questions.

• Build semantic models, dashboards, and analytical tools using Power BI or a comparable enterprise business intelligence platform.

• Enable FP&A users to independently answer recurring business questions with trusted data, reducing manual extracts, spreadsheet-based preparation, and one-off reporting.