Data Analyst role

Data Analyst

Finance Analytical Assets | Enterprise Data Products

Experience
5-7 Years

Primary Platform
Azure Databricks

Focus
Reusable & AI-Ready Assets

About the Role

Are you a data professional who wants to build things that last, rather than churning out one-off reports? We are looking for a Senior Data Analyst to join our Finance Data Office - Analytics team and become part of a capability that turns raw enterprise data into trusted, reusable and AI-ready analytical assets.

You will sit at the heart of our data ecosystem the strategic layer that connects Data Engineering, Reporting/BI and Data Science with the Finance business. Your job is to make data meaningful: defining the metrics people trust, building the models everyone reuses, and creating the foundations that power dashboards, self-service analytics and future AI use cases. This is a builder role, not a dashboard-formatting role.

What Youll Do

• Work with Finance and Treasury stakeholders to understand their real problems, then translate them into clear analytical requirements, KPIs and business rules.
• Design and own the semantic layer certified KPI definitions, fact and dimension models, and the single source of truth for business metrics.
• Build reusable analytical assets in Azure Databricks using SQL, Python and PySpark – curated gold-layer datasets, Databricks SQL views and modular transformation logic.
• Profile, validate and reconcile data so the numbers can be trusted, and document what they mean, where they come from and where their limits are. Perform deep-dive data analysis and exploratory data mining to uncover insights and trends.
• Prepare clean, well-documented, context-rich datasets that Data Science and AI/Copilot use cases can safely rely on. Assets should be future and AI ready for conversational analytics.
• Collaborate with data engineers and architects to design and implement efficient data pipelines in the Databricks platform. Work hand-in-hand with Reporting/BI and Data Science teams – supporting them without duplicating their work.
• Create clear and concise technical documentation for developed code, data transformations, and performance enhancements.
• Stay up to date with industry trends and best practices in data processing, performance optimization, and cloud technologies

What You’ll Bring

Must-Haves

• 5–7 years in data analytics, analytics engineering, BI engineering, data modeling or a related field.
• Strong hands-on Azure Databricks experience (Delta tables, notebooks, Databricks SQL, Databricks Gene, Apps).
• Advanced SQL – window functions, CTEs, complex joins, reconciliation and performance-aware querying.
• Solid Python / PySpark skills for profiling, cleansing, enrichment and large-scale transformation.
• Real experience with dimensional modeling – fact/dim design, star schema, grain and conformed dimensions.
• A quality and governance mindset – validation, reconciliation, documentation and version control (Git / Azure DevOps).
• The ability to talk to business stakeholders, challenge unclear asks, and turn ambiguity into structured requirements.

Nice-to-Haves

• Unity Catalog or other enterprise data cataloging experience.
• Exposure to Finance, Treasury, Cash Management or Working Capital analytics.
• Familiarity with SAP ECC / S4HANA finance data structures.
• Experience preparing datasets for AI / ML or predictive analytics.
• Awareness of Power BI semantic models and how curated datasets are consumed downstream.