Senior Data Analyst

Access•Peabody, MA

About The Position

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.

Requirements

Advanced SQL and strong dimensional and analytical data-modeling skills.
Proficiency in Python for data processing, automation, and advanced analytics.
Experience developing or supporting ETL/ELT pipelines and reusable analytical datasets.
Hands-on experience with a modern cloud data warehouse or lakehouse environment.
Experience with data reconciliation, quality assurance, troubleshooting, and documentation.
Experience developing semantic models, dashboards, or business intelligence solutions.
Ability to translate ambiguous business requirements into scalable analytical and technical solutions.
Familiarity with source control, testing, deployment, and production support practices.
Bachelor's degree in a technical, quantitative, analytical, Finance, or business discipline; equivalent relevant professional experience may be considered.
Five or more years of relevant experience in data analytics, analytics engineering, data engineering, business intelligence, or a related field.
Demonstrated experience independently delivering complex data and analytical solutions.

Nice To Haves

Finance, FP&A, commercial analytics, or executive reporting experience is helpful but not required.
Experience with Microsoft Fabric and Azure data services, including lakehouses, warehouses, notebooks, pipelines, or related analytical workloads.
Power BI experience, including semantic modeling and DAX.
Experience with Git, CI/CD, orchestration, monitoring, or production support.
Experience supporting Finance, FP&A, commercial analytics, or executive reporting.
Familiarity with forecasting, customer analytics, revenue analytics, or portfolio analytics.
Experience working with centralized or shared enterprise data teams.

Responsibilities

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.
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.
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.
Use approved AI-assisted development tools to improve coding, testing, documentation, and analytical productivity while validating outputs and protecting confidential data.