Senior Business Data Analyst

At Siemens, we help organizations transform maintenance and operations through connected insights, AI-powered technology, and intelligent asset management solutions. Our software enables customers to manage the full lifecycle of assets, facilities, and infrastructure while improving efficiency, reducing risk, and optimizing long-term investments. By connecting data, people, and processes, we empower organizations to make smarter decisions, maximize asset performance, and achieve more resilient operations.

Description

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.

You’ll make an impact by

Business Partnership and Requirements Engineering

• 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.

Data Analysis and Insight Generation

• 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.

Data Products and Business Intelligence

• 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.

Data Modeling and Technical Delivery

• 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.

End to End Solution Ownership

• 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.

Data Quality and Governance

• 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.

This is how you'll win us over 

• 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.

You'll Thrive Even More If You Also Bring

• 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.
  

What Success Looks Like

Within the first 6 to 12 months, the successful candidate should be able to:

• Become a trusted analytics partner capable of independently leading complex business initiatives from discovery through business impact.
• Build trusted relationships with stakeholders at all levels, from field teams to senior leadership, in one or more commercial domains.
• Handle a steady flow of business requests with insights and recommendations that stakeholders act on.
• 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 and identify appropriate data sources, models, and dependencies.
• Become a trusted owner of business logic, data investigation, and analytical delivery within assigned domains.
• Collaborate effectively with Data Engineering, Data Science, AI, and other specialist functions.
• Produce clear documentation covering business definitions, data sources, analytical logic, lineage, and solution design.
• Contribute to a scalable and resilient Data & AI environment through strong ownership, reusable solutions, and knowledge sharing.
• Define measurable success criteria and experimentation approaches to evaluate business initiatives, campaigns, and AI-driven solutions

Qualified Applicants must be legally authorized for employment in the United States. Qualified Applicants will not require employer sponsored work authorization now or in the future for employment in the United States.    

At Siemens, you'll have the opportunity to grow your career while helping organizations operate smarter, safer, and more sustainably. We foster a culture of innovation, collaboration, and continuous learning, where employees are empowered to make a difference every day. If you're excited about solving real-world challenges and shaping the future of asset management, we encourage you to apply.

Our Commitment to Equity and Inclusion in our Diverse Global Workforce:

We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the every day with us.

Siemens maintains a Drug Free workplace in accordance with applicable law