Data Analytics Engineer at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Apr, 26

Salary

234700.0

Posted On

15 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Modeling, Statistical Analysis, Machine Learning, Data Extraction, Data Transformation, Collaboration, Data Integration, Performance Monitoring, SQL, Data Visualization, Generative AI, Data Quality, Business Insights, Data Cataloging, Data Warehousing, Automation

Industry

Software Development

Description

Overview Microsoft Cloud Operations and Innovation (CO+I) is the engine that powers our cloud services. Our infrastructure is comprised of a large global portfolio of more than one hundred datacenters and one million servers. Our foundation is built upon and managed by a team of subject matter experts, who work tirelessly to support digital services for more than one billion customers and twenty million businesses in over ninety countries worldwide. Within CO+I, the Core Datacenter Services (CDS) team is responsible for improving overall availability and efficiency for Microsoft’s cloud business. We have multiple teams focused on datacenter performance and all aspects of datacenter utilization effectiveness that includes power, water, supply chain, and labor. We are seeking a Data Analytics Engineer to take on analyzing complex business problems over a large data estate that will guide the business in formulating solutions while also helping discover new opportunities in the context of datacenter operations and supply chain. You will build everything required to uncover and generate actionable insights that move the needle. The ideal candidate will have previous experience with data modeling, statistical analysis, and presenting succinct summaries of findings to drive action. Responsibilities Build and Analyze: Develop and build datasets in collaboration with other data engineers, analysts, and scientists that support AI/ML pipelines, data cataloging, and a federated reporting ecosystem. Design and implement statistical and machine learning methods to measure business impact and uncover actionable insights proactively. System Architecture: Architect and implement scalable integration solutions used to drive warehouse and datacenter operations. Data Extraction and Integration: Extract raw data from multiple sources using query languages, tools, or machine learning algorithms. Ensure data accuracy, validity, and reliability. Contribute to code reviews and drive the business case for advanced orchestration techniques. Data Transformation: Develop and use advanced techniques to transform raw data into compatible formats for downstream sources. Expand the application and reusability of software and tools. Drive efficiencies in data extraction to ensure quality and completeness. Stakeholder Collaboration: Collaborate with stakeholders to recommend data requirements. Partner with business teams to determine data costs, access, usage, and availability. Negotiate agreements with partners and system owners for project delivery and data ownership. Data Modeling: Design data models that meet business requirements and translate business needs into design specifications. Lead conversations with stakeholders to improve data models and schemas. Develop solutions considering analytical requirements and compute/storage consumption. Mine for Opportunities: Drive acquisition, management, and analysis of large-scale datasets to inform the business of the highest priority opportunities to target (e.g. – cost savings, operational efficiency, availability impact). Performance Monitoring: Ensure effective performance monitoring across data pipelines. Build automation into data visualizations and aggregations to monitor data quality and pipeline health. Develop troubleshooting guides and operating procedures for addressing complex problems flagged by automated testing. Qualifications Required: Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field OR equivalent experience. Background Check Requirements: Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Preferred Qualifications: Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) 3+ years designing data models for operations and supply chains in a distributed data environment. 2+ years of writing SQL/KQL, MS Fabric, or equivalent data transformation tools. 1+ years experience building and/or evaluating generative AI based solutions Ability to translate complex business insights into clear and actionable recommendations for business teams. Familiarity with building and analyzing core business metrics. Understanding of database systems with the ability to independently acquire, transform, and store data. Proficient in designing and implementing Generative AI pipelines (e.g., RAG systems, domain/task-specific finetuning) Data Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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 This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. 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 with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

The Data Analytics Engineer will analyze complex business problems and develop datasets to support AI/ML pipelines and reporting ecosystems. They will also collaborate with stakeholders to recommend data requirements and ensure effective performance monitoring across data pipelines.