New 
Senior Data Analyst
 Microsoft | |
 United States, Nevada, Reno | |
| 6840 Sierra Center Parkway (Show on map) | |
 May 20, 2025 | |
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OverviewThe Scaled Solutions and Insights Data Analyst mission is to empower the Operations Service Center stakeholders with a range of data products and technical solutions to help them gather deeper insights and intelligence to amplify business outcomes in alignment to core priorities and strategic planning. We are looking for aSenior Data Analystwho is willing to work in a dynamic environment. You will need to be adept at managing business change, evolving requirements, adjustments in a strategic direction, and emerging technologies. This is an amazing opportunity to be at the center of building a showcase worthy data capability, the successful candidates should have proven experience in working with ambiguity, program management, a understanding of business and engineering priorities, analytical, financial, organizational and delivery skills. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
ResponsibilitiesBusiness and Data Landscape: Apply in-depth knowledge of the business, evolving data landscape, tools, and technologies to link business topics to relevant data sources and external trends. Anticipate data and business requirements, develop data frames and analytical solutions, and identify opportunities to enhance or automate data infrastructure and analyses. Customer/Stakeholder Orientation: Understand customer needs and perspectives, validate requirements, and deliver accessible data insights and tools. Build trust by leveraging knowledge of Microsoft products and solutions, interpreting data within relevant contexts, and articulating key details to drive realistic customer expectations. Expertise in Data: Applies expertise in data sources, formats, and quality to identify and leverage data across multiple sources, understands data requirements, and evaluates the sufficiency of data for addressing relevant and impactful business questions. Determines and leverages optimal methods and tools for integrating data and proactively works to identify and address data integrity, quality, and/or access issues. Recommends opportunities to build new data pipelines or integrations to better meet requirements, and initiates collaborative action to source additional data. Develops and/or recommends initial/prototype data models and/or tools for others' consumption, leverages relevant data and frameworks from other teams, and escalates complex issues with data or data models to appropriate Engineering or Data-Science teams. Data Analysis: Applies expertise in data, business, and customer needs to evaluate and determine ideal analytical and statistical techniques to address business and/or research questions. Guides and establishes partnerships with others to execute complex analyses, resolve analytical challenges, interpret results across relevant contexts, and provide actionable recommendations. Critically evaluates the choice of tools, techniques, and assumptions to highlight potential gaps and ensure they are utilized appropriately within context, that outcomes align with business and/or research needs, and provides feedback on features and functions of analytical tools and/or models. Anticipates the risks of data leakage, analytical tradeoffs, methodological limitations, etc., and can guide teammates on solutions. Reporting and Sharing Results: Share insights through dashboards, reports, data visualizations, and interactive self-service platforms. Synthesize and simplify details across analyses to highlight relevant findings and inform business decisions. Experimentation and Innovation: Design and execute formal experiments or prototypes to evaluate the impact of new features or processes. Partner cross-functionally to advise on experimental design and evaluation frameworks, and make data-driven recommendations for strategic business goals. Improvement and Efficiency: Promote methods for efficient analytics and reporting, automate ad-hoc analyses, and participate in peer reviews to ensure quality and relevance. Recommend and socialize optimal methods for operationalizing, sharing, and scaling insights. Shares critical domain expertise to create clarity, ensure readiness to appropriately consume and leverage data and/or insights, and evaluate the viability of automated methods for use in data collection, reporting, and/or analysis. Data Model Evaluation: Understands and evaluates the relationship between analytical model(s) and business objectives, highlight gaps, and presents findings to senior stakeholders. Establish clear linkage between generated data models and desired business objectives. Coaches and mentors less experienced analysts as needed. Orchestration and Collaboration: Collaborate with internal stakeholders to ensure quality execution of data sourcing, analysis, and the adoption of best practices. Leverage expertise to identify areas for innovation and address evolving business needs. Data Privacy and Governance: Maintain expertise in data privacy and security requirements, ensure compliance with regulations, and enforce standards related to data usage and handling. Guide others to uphold and apply updated data privacy and governance standards. Other Embody our culture and values | |