Senior Data Analyst

Job description

Innovation starts from the heart. Our Enterprise Data, Analytics & AI Integration teams help Edwards make better, faster, and more connected decisions by turning business needs into trusted data, analytics, and AI-enabled solutions. If you are passionate about solving business problems through data, working across functions, and helping teams unlock measurable value, this is an opportunity to grow your career in a highly collaborative and impactful environment. This role sits at the intersection of business analysis, data product management, analytics delivery, and stakeholder engagement. You’ll support assigned business domains by helping translate business needs into clear, actionable requirements and supporting the delivery of scalable data and analytics solutions. This is a hands-on senior analyst role within the Data Product & Engagement team for someone who enjoys working closely with business and technology teams, thrives in ambiguity, and can independently drive clarity across requirements, data analysis, testing, and adoption. You’ll influence outcomes through strong analytical thinking, business partnership, and execution discipline. If you think like a Business Systems Analyst, understand data deeply, and operate with a product mindset, this role was built for you. This role is based in our Prague office, with home office flexibility. It offers a truly global scope, with regular collaboration and daily interaction with our teams in the US, giving you the opportunity to partner with international stakeholders and contribute to high-impact initiatives across the organization. How you’ll make an impact: Serve as a key techno-functional analyst for data, analytics, and reporting needs across assigned business domains. Partner with business stakeholders to understand objectives, pain points, processes, and data needs. Translate business needs into clear requirements, user stories, acceptance criteria, process flows, and functional specifications. Support Data Product Managers in intake, prioritization, roadmap planning, and value-based demand shaping. Analyze data from multiple sources to validate business logic, identify gaps, troubleshoot issues, and support solution design. Work closely with architects, data engineers, analytics engineers, and reporting teams to ensure requirements are clear, feasible, and aligned to enterprise standards. Support creation and refinement of data definitions, business rules, KPIs, metrics, and reporting logic. Participate in data validation, UAT planning, test case development, defect triage, and release readiness activities. Help drive adoption and value realization by supporting stakeholder communication, rollout planning, training materials, usage feedback, and continuous improvement. Proactively identify risks, dependencies, data quality concerns, and requirement gaps that may impact successful delivery. Contribute to DP&E templates, standards, and best practices for requirements, intake, documentation, and stakeholder engagement. Support business users in understanding available data products, dashboards, reports, and self-service analytics capabilities. A Day in the Life: No two days look the same, but a typical day may include: Meeting with business stakeholders to clarify reporting, analytics, or data product needs. Translating business conversations into structured requirements, user stories, and acceptance criteria. Reviewing source data, SQL outputs, dashboards, or reports to confirm accuracy and business meaning. Partnering with engineering and architecture teams to clarify data logic, dependencies, and delivery approach. Refining backlog items with delivery teams to reduce ambiguity before implementation begins. Supporting UAT by coordinating feedback, validating results, and helping resolve defects. Working with Data Product Managers to assess intake requests, business value, priority, and readiness. Documenting KPIs, business rules, process flows, and data definitions for assigned initiatives. Helping business users understand delivered solutions and identifying opportunities to improve adoption. Raising risks, gaps, or dependency concerns early so the team can address them before delivery impact. What Makes This Role Different Work on enterprise-scale data and analytics initiatives that support important business processes in a highly regulated, high-impact industry. Operate as a bridge between business stakeholders and technical delivery teams. Help shape what gets built by ensuring requirements are clear, complete, and tied to business value. Gain exposure to modern data platforms, analytics engineering, BI, and emerging AI-enabled use cases. Build strong techno-functional expertise across business domains, data products, and enterprise analytics capabilities. Contribute to the continued maturity of the Data Product & Engagement operating model. What you’ll need (Required) Bachelor’s degree in Information Systems, Computer Science, Business Analytics, Engineering, Data Analytics, or a related field. 5+ years of experience in business analysis, data analysis, analytics delivery, business systems analysis, or a related discipline. Proven ability to translate business needs into clear, executable requirements and user stories. Strong SQL skills for data exploration, validation, troubleshooting, and problem solving. Experience working with reporting, analytics, BI, data warehousing, or enterprise data platforms. Strong understanding of business processes, data flows, KPIs, and reporting logic. Ability to work independently on moderately complex initiatives with limited supervision. Experience supporting Agile delivery teams, backlog refinement, UAT, and release readiness. Strong communication skills with both technical and business audiences. Strong analytical thinking, attention to detail, and problem-solving skills. Ability to manage multiple priorities, stakeholders, and deliverables in a fast-paced environment. What else we look for (Preferred) Experience with modern cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, Azure, AWS, or similar technologies. Experience with Power BI or other enterprise BI and visualization tools. Familiarity with data product management, data-as-a-product concepts, or product-oriented delivery models. Experience documenting business rules, metric definitions, semantic layer requirements, or data quality expectations. Exposure to data governance, metadata management, master data, or enterprise reporting standards. Experience in healthcare, medical device, life sciences, or another regulated industry. Exposure to AI, advanced analytics, automation, or self-service analytics use cases. Ability to facilitate workshops, lead stakeholder discussions, and build alignment across business and technical teams.