Senior Business Data Analyst

Siemens•Raleigh, NC

3d•$89,795 - $207,767•Hybrid

About The Position

The Senior Business Data Analyst translates business needs into scalable, high-quality data and analytics solutions. This is a hands-on senior analytics role combining strong business ownership with technical depth. You will work directly with stakeholders to understand business problems, clarify outcomes and requirements, investigate data, generate insights, and deliver analytical products from discovery through implementation. You will join the Smart Infrastructure, Asset Management Software Information Technology (SI AMS IT) Data & AI team and work across Sales, Marketing, Customer Success, Finance, and other commercial functions. Marketing will be an important area of focus, making previous experience in Marketing Analytics or related commercial domains particularly valuable. The role offers significant ownership and visibility within a small, collaborative Data & AI team. You will work with modern technologies including Snowflake, dbt, SQL, Python, Power BI, and Qlik, while contributing to the continued evolution of our analytics, data products, and AI capabilities. We expect you to be an early adopter of generative AI, using it to accelerate delivery and expand into adjacent technical areas. This role is ideal for an experienced analyst who enjoys owning the full analytics lifecycle, from understanding business problems and defining requirements through data modeling, analysis, visualization, and stakeholder adoption.

Requirements

7+ years of experience in data analytics, business intelligence, analytics engineering, or a 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 experience working with relational data, transformations, data quality, validation, and complex datasets.
Demonstrated experience partnering with business stakeholders to translate business problems into analytical requirements and pragmatic solutions.
Proven track record of uncovering insights through data mining and analysis, and applying business acumen to recommend actions that shaped 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, such as opportunities, accounts, leads, and campaigns.
Experience working 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, with the ability to work independently and navigate ambiguity.

Nice To Haves

Experience working in smaller or lean analytics teams where individuals are responsible for multiple stages of the analytics lifecycle, from requirements gathering through solution delivery
Experience supporting Marketing Analytics or other commercial functions such as 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, such as 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.

Responsibilities

Partner with business stakeholders to understand problems, desired outcomes, success criteria, and business value.
Handle ongoing business requests as a trusted partner, not a ticket taker, focusing on the business need behind each ask.
Translate business needs into clear, actionable data and analytics requirements.
Resolve missing or ambiguous requirements and break larger problems into manageable use cases.
Build strong understanding of assigned business domains, processes, metrics, and business logic.
Lead discovery, solution demonstrations, validation, and business sign off for assigned use cases.
Collaborate with global stakeholders across North America and Europe, with core working hours aligned to Eastern Time business operations.
Extract, combine, analyze, and validate complex datasets using advanced SQL and analytical tools.
Identify trends, patterns, anomalies, and actionable business insights.
Apply statistical and analytical methods to support business decisions.
Assess data quality, completeness, and fitness for purpose.
Translate analytical findings into clear recommendations for stakeholders.
Use generative AI tools, such as Claude Code or GH Copilot, to accelerate SQL, dbt, and Power BI development, and share what works with the team.
Design and develop dashboards, visualizations, and analytical products using Power BI, Qlik, and other relevant tools.
Create intuitive data experiences that enable stakeholders to act on insights.
Contribute to reusable data products, shared metrics, and standardized analytics solutions.
Support use cases across 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 individual use cases.
Work with platforms such as Snowflake and tools such as dbt.
Investigate analytical data issues and partner with Data Engineering on production pipelines and shared models.
Own assigned data use cases from initial business problem through delivery, validation, and adoption.
Act as solution lead and coordinate contributions from Data Engineering, Data Science, BI, AI, and other specialists.
Proactively manage dependencies, risks, and data availability constraints.
Share knowledge and 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.