Senior Business Data Analyst

Translate business priorities into scalable data and analytics solutions with a marketing analytics focus.

Responsibilities

• Partner with business stakeholders to define problems, desired outcomes, success criteria, and business value.
• Act as a trusted partner for ongoing business requests, focusing on the business need behind each ask.
• Convert business needs into clear, actionable data and analytics requirements.
• Resolve missing or ambiguous requirements and break larger problems into manageable use cases.
• Build domain depth in assigned areas, including processes, metrics, and business logic.
• Lead discovery, solution demonstrations, validation, and obtain business sign off for assigned use cases.
• Collaborate with global stakeholders across North America and Europe, aligned to Eastern Time business operations.
• Extract, combine, analyze, and validate complex datasets using advanced SQL and analytical tools.
• Identify trends, patterns, anomalies, and generate actionable business insights.
• Apply statistical and analytical methods to support business decisions.
• Assess data quality, completeness, and fitness for purpose.
• Translate analytical findings into recommendations stakeholders can act on.
• Use generative AI tools (such as Claude Code or GH Copilot) to accelerate SQL, dbt, and Power BI development, and share learnings with the team.
• Design and develop dashboards, visualizations, and analytical products using Power BI, Qlik, and related tools.
• Create intuitive data experiences that enable stakeholder action on insights.
• Contribute to reusable data products, shared metrics, and standardized analytics solutions.
• Support use cases including customer segmentation, marketing effectiveness, revenue analytics, retention, and predictive analytics.
• Understand source systems, warehouse structures, data relationships, business definitions, and analytical data models.
• Create or adapt analytical data models and transformations for specific use cases.
• Work with Snowflake and tools such as dbt.
• Investigate analytical data issues and collaborate with Data Engineering on production pipelines and shared models.
• Own use cases end-to-end from business problem definition through delivery, validation, and adoption.
• Coordinate contributions from Data Engineering, Data Science, BI, AI, and other specialists as solution lead.
• Manage dependencies, risks, and data availability constraints proactively.
• Reduce dependency on individual experts through office hours, training, self-service reporting, and recurring metric deep dives.
• Apply established data standards, definitions, governance requirements, and documentation practices.
• Identify data quality risks and coordinate resolution with appropriate owners.
• Maintain awareness of security, privacy, responsible data usage, and enterprise governance requirements.
• Support traceability and consistency of important business metrics and analytical outputs.

Requirements

• 7+ years of experience in data analytics, business intelligence, analytics engineering, or related field, or equivalent demonstrated experience.
• Advanced SQL skills, including complex joins, common table expressions, window functions, and analytical transformations.
• Strong analytical and data modeling capabilities, including work with relational data, transformations, data quality, validation, and complex datasets.
• Proven ability to partner with business stakeholders to translate business problems into analytical requirements and pragmatic solutions.
• Track record of uncovering insights through data mining and analysis and using business acumen to shape decisions.
• Experience developing dashboards, visualizations, or analytical applications using Power BI, Qlik, or comparable BI platforms.
• Hands-on experience with Salesforce or other CRM data (opportunities, accounts, leads, campaigns).
• Experience with a modern cloud data platform such as Snowflake, Databricks, Azure, AWS, or comparable technologies.
• Ability to independently investigate business and data problems and deliver analytical solutions with limited supervision.
• Strong communication and collaboration skills, including navigating ambiguity.

Additional Information

• Location: Raleigh, NC (onsite)
• Salary: USD 89,795 - 207,767 per year
• Core technologies: SQL, Python, Power BI, Qlik, Snowflake, dbt, Claude Code, GH Copilot, Salesforce, Databricks, Azure, AWS

You'll Thrive If You Also Bring

• Experience in smaller or lean analytics teams responsible for multiple stages of the analytics lifecycle from requirements gathering through delivery.
• Marketing Analytics experience or exposure to Sales, Customer Success, Revenue Operations, or Commercial Finance.
• Hands-on experience with Snowflake and dbt (or comparable data transformation tooling).
• Python experience for data analysis, automation, or analytical development.
• Experience with go-to-market systems data including marketing automation, attribution, and customer success platforms.
• Familiarity with dimensional modeling, semantic data models, and data pipelines.
• Exposure to machine learning, predictive analytics, segmentation, AI-enabled use cases, or enterprise data governance.
• Familiarity with prompt engineering, AI-assisted analytics, predictive modeling, experimentation frameworks, or model performance evaluation.
• Experience supporting SaaS, subscription, software, or recurring revenue business models.

What Success Looks Like

• Become a trusted analytics partner who can independently lead complex initiatives from discovery through business impact.
• Build trusted stakeholder relationships across field teams and senior leadership within one or more commercial domains.
• Handle a steady flow of business requests with actionable insights and recommendations.
• Turn loosely defined business problems into structured, executable analytical solutions.
• Deliver reliable analysis, data models, and BI solutions with limited day-to-day support.
• Navigate SI AMS data structures to identify appropriate data sources, models, and dependencies.
• Own business logic, data investigation, and analytical delivery within assigned domains.
• Collaborate effectively with Data Engineering, Data Science, AI, and other specialists.
• Produce clear documentation for business definitions, data sources, analytical logic, lineage, and solution design.
• Contribute to a scalable and resilient Data & AI environment through reusable solutions and knowledge sharing.
• Define measurable success criteria and experimentation approaches to evaluate initiatives, campaigns, and AI-driven solutions.