Microsoft Cloud Operations + Innovation (CO+I) is the team behind the cloud. CO+I is responsible for delivering over 200 Microsoft web portals, Live and Online Services around the world including infrastructure, security and compliance, operations, globalization, and manageability. Our focus is on smart growth, high efficiency, and to deliver trusted experience to customers and partners worldwide.

Our datacenter delivery team is looking for a data professional to bring their skills to the construction quality space. Our team maintains and governs processes, delivers data, metrics, and analytics, and provides process automation to improve our predictability and quality in our datacenters. Th e Data Engineer - Construction Quality will be part of the global team and lead the data and metrics program to provide analytics and insights, automation, and drive platform maturity to our tooling, applications, and suite of visualization and reporting products.

Our ideal candidate will have:

• In-depth knowledge of the business, its data landscape, and the lineage of our data across multiple areas

• A customer- and/or stakeholder-oriented focus by understanding their needs and perspectives.

• Expertise in data sources 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.

• Expertise in data, business, and customer needs to evaluate and determine ideal analytical and inferential techniques to address business questions.

They will also:

• Share insights and analytical expertise to tell stories of analyses through one or more means.

• Builds, supports, and/or consults others on the execution of formal experiments or prototypes/proofs of concepts, to evaluate the impact of new or changed features or processes.

• Identifies and promotes methods that create efficiency in core work related to analytics and reporting that are reusable, readily discoverable by decision-makers, self-service, and directed to meaningful interpretation of data and driving business decisions.

• Understands the relationship between analytical model(s) and business objectives.

• Maintains expertise in data privacy requirements, responsible and ethical data handling practices, and models compliance with classification and governance rules and regulations.

Some useful skills they may possess:

Algorithm Design and Implementation - The ability to design, analyze, implement, optimize, profile and experimentally evaluate computer algorithms. Includes knowledge of practical applications of algorithms in software engineering.

Analytical Skills - The ability to review quantitative or conceptual problems and situations to draw appropriate and valid conclusions from data presented. This includes analyzing data to determine the most significant elements, identify common elements and themes in situations and actions, and recognize cause and effect relationships.

Automation - Knowledge of automation technologies, methods, and processes used for quality and cost improvements.

Big Data - Knowledge of and ability to use big data, which includes ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. This includes data that is characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation to value.

Code Review - The ability to apply knowledge about program coding to perform peer reviews of computer source code with the intent to find and fix mistakes, find security loopholes, and correct inefficiencies overlooked in the initial development phase, improving overall code quality.

Compliance Oversight - The ability to comply with the rules, regulations, codes, sanctions and other statutory requirements, guidelines and instructions as defined by governing bodies and organizations that regulate and/or set standards or telecommunications and data security/privacy.

Conflict Resolution - The ability to manage conflict, disharmony, and strife among people and situations, while recognizing and addressing sensitivities.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Responsibilities

Customer/Stakeholder Orientation

Applies a customer- and/or stakeholder-oriented focus by understanding their needs and perspectives. Validates and advises customer and/or stakeholder requirements, focusing on broader customer organization/context and enables customer adoption by delivering solutions and supporting relationships. Works with customers and/or stakeholders to overcome obstacles, develop tailored and practical solutions, and ensure proper execution. Builds trust with customers and/or stakeholders by leveraging the knowledge of Microsoft products and solutions, interpreting data within relevant contexts, and articulating key details to drive realistic customer expectations and an understanding of the limitations of their data.

Orchestration and Collaboration

Leverages working relationships within and across teams to ensure alignment and quality execution of data sourcing, methods, model development and application, and the appropriate use of analytical tools and processes. Drives the adoption of recommended data sources and analysis practices to address business priorities and deliver key insights and results. Proactively engages stakeholders to identify and act on opportunities to leverage data, resources, and solutions that were instrumental to success in similar contexts and consults across teams on decisions related to data sourcing, analyses, and the interpretation of analytical results.

Reporting and Sharing Results

Shares insights and analytical expertise to tell stories of analyses through one or more means, including dashboards, reports, data visualizations, self-service platforms, slides, internal forums, ad-hoc inquiries, and talking points that highlight relevant insights. Synthesizes and simplifies details across analyses and reporting platforms to highlight the most relevant findings that can help inform business decisions and identifies opportunities to improve the efficiency of insights reporting techniques. Guides others and establishes partnerships with stakeholders to ensure results are accessible, and can provide information accurately, clearly, and with sufficient relevance to influence decision making for intended audience(s).

Business and Data Landscape

Applies in-depth knowledge of the business, its data landscape, and the lineage of those data across multiple areas. Links business topics to relevant data sources and external trends, anticipates data and business requirements, and probes for further insights into relevant business- or data-related topics to support data sourcing and integration decisions. Proactively anticipates business questions, develops data frames and analytical solutions, builds connections across business areas, and identifies and acts on opportunities to develop existing, enhanced, or automated data infrastructure, analyses, and solutions that enable the evaluation of business questions. May coach others to help them broaden their knowledge of the business and relevant data sources.

Improvement and Efficiency

Identifies and promotes methods that create efficiency in core work related to analytics and reporting that are reusable, readily discoverable by decision makers, self service, and directed to meaningful interpretation of data and driving business decisions. Recommends and socializes optimal methods for operationalizing, sharing, and scaling insights, shares expertise and a practical rationale for when ad-hoc analyses should become part of regular reporting features. 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.

Experimentation and Innovation

Builds, supports, and/or consults others on the execution of formal experiments or prototypes/proofs of concepts, to evaluate the impact of new or changed features or processes. Partners cross-functionally to advise on experimental design or evaluation frameworks for established and/or emerging data sources, as well as decisions related to data use, personalization, and to ensure inferences are appropriate to the data and design when interpreting results. Assesses and takes calculated risks, and applies previous learnings to influence mitigation plans. Synthesizes and connects results across experiments, identifies relevant connections to other work, and makes data-driven recommendations for next steps with clear links to strategic business goals. Determines and recommends optimal and innovative methods for operationalizing, sharing, and scaling experimental insights.

Data Analysis

Applies expertise in data, business, and customer needs to evaluate and determine ideal analytical and inferential techniques to address business 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 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.

Data Privacy and Governance

Maintains expertise in data privacy requirements, responsible and ethical data handling practices, and models compliance with classification and governance rules and regulations. Enforces team standards related to bias, privacy, and ethics, and ensures data have undergone appropriate Corporate, Executive, and Legal Affairs (CELA) reviews and ensures work activities and results are in alignment with principles and controls. Knows where to seek expertise on data privacy rules and regulations and shares personal knowledge of them with peers as needed to exemplify and enforce standards related to bias, privacy, and ethics. Identifies and addresses impact of updated guidance on work activities and results.

Expertise in Data

Applies expertise in data sources 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 optimal methods 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.

Model Evaluation

Understands relationship between analytical model(s) and business objectives. Establishes clear linkage between generated models and desired business objectives to assesses the degree to which models meet business objectives and highlights gaps or areas that have been missed. Ensures alignment on definitions and standards across stakeholders and defines, designs, and promotes the use of appropriate feedback and evaluation methods. Coaches and mentors less experienced engineers as needed. Presents results and findings to senior stakeholders.

Other

• Embody our Culture (https://www.microsoft.com/en-us/about/corporate-values) and Values (https://careers.microsoft.com/us/en/culture)

Qualifications

Required Qualifications

• Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis

• OR Master's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis

• OR equivalent experience.

• 2+ years’ experience in visualization and reporting: experience with Power BI for generating dashboards that track metrics, trends, and performance.

• 2+ years’ experience using data for problem solving and to drive process improvements and eliminate inefficiencies.

Preferred Qualifications

• 2+ years’ experience with cross-functional collaboration : demonstrated experience in collaborating with engineers, manufacturing or build/delivery teams, or other departments to troubleshoot quality issues.

• Proficiency in Programming Languages : Technical proficiency with Python, R, or SQL for data manipulation and analysis, especially with libraries for data science and machine learning (e.g., pandas, NumPy, scikit-learn).

• Previous experience working in or around data centers

• Quality Control or Quality Assurance Background: previousexperience in or supporting QC/QA teams, especially in industries like manufacturing, pharmaceuticals, or technology

• Experience with Compliance and Regulatory Standards : Familiarity with standards such as ISO 9001 (Quality Management Systems) or Six Sigma methodologies

• Experience in Monitoring Production Metrics : understanding key performance indicators (KPIs) related to product quality, defect rates, and process efficiency

• Database Management : Proficiency with databases (SQL, NoSQL, or cloud-based data warehouses) is essential for managing large volumes of QC data

• Statistical Analysis for Quality Control : Familiarity with statistical process control (SPC), root cause analysis, or regression analysis to identify trends and deviations in production quality.

• Predictive Analytics : Experience in developing predictive models to anticipate defects or production issues before they occur.

• Root Cause Analysis : Candidates with experience in identifying the root causes of quality issues using data-driven methods (like 5 Whys or Fishbone Diagrams) can significantly impact your QC organization.

• Communication Skills : The ability to communicate technical findings in a non-technical manner is crucial for ensuring that QC data is actionable for management and operations teams.

Data Analytics IC4 - The typical base pay range for this role across the U.S. is USD $103,800 - $200,300 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,700 - $219,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until November 21, 2024.

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Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .