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. #J-18808-Ljbffr Siemens