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 contemporary 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’llmake 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,

📌 Senior Business Data Analyst (Winnipeg)
🏢 Siemens
📍 Winnipeg