Data Engineer, Global Talent Intelligence

Overview

We are hiring a Data Engineer to support the Global Talent Intelligence team. The Global Talent Intelligence (GTI) team sits at the intersection of talent, technology, and strategy-partnering with senior leaders across Microsoft to anticipate market shifts and inform high-impact business decisions. At the core of this work is a purpose-built talent data engine that transforms vast, complex external signals into clear, actionable intelligence.

As aData Engineer on GTI, you will drive the next iteration of design and scale for the infrastructure that powers how Microsoft relies on talent movement as a leading indicator of market movement. This role goes beyond traditional data engineering: you will build curated pipelines, models, and power interfaces that enable GTI to move from manual analysis to proactive and predictive insights-unlocking earlier visibility into where skills are emerging, where competition is intensifying, and where strategic intervention matters most.

Our GTI data engineer works across an unusually rich and diverse signal ecosystem, synthesizing supply and demand dynamics, competitor and investment signals, innovation indicators, emerging talent and skills, and geo-specific workforce risk factors. These signals power tools and intelligence products that directly influence executive decision-making-from workforce planning and site strategy to critical skill investments and long-term growth bets.

This is a highly collaborative role, partnering closely with talent intelligence consultants, analysts, and business stakeholders to ensure data is not only technically sound, but strategically meaningful-moving insights into action.

Success in this role requires:

• Strong data engineering fundamentals, paired with modern AI adoption and automation practices
• A product mindset for designing systems and interfaces that scale, evolve, and drive action
• Curiosity about how talent dynamics shape markets, strategy, and competitive advantage
• The ability to create clarity from complexity and translate insight into direction
• Dedication to building secure, compliant, and validated data systems
• Creativity and discipline in designing for optimization, reliability, and scale

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.

Responsibilities

Compliance

• Anticipates the need for data governance and designs data modeling and data handling procedures, with direct support and partnership with Corporate, External, and Legal Affairs (CELA), to ensure compliance with applicable laws and policies across all aspects of the development process.
• Tags data based on categorization (e.g., personally identifiable information [PII], pseudo-anonymized, financial).
• Documents data type, build data dictionary, classifications, and lineage to ensure traceability.
• Governs accessibility of data within assigned data pipelines.
• Provides guidance on contributions to the data glossary to document the origin, usage, and format of data for each program.
• Independently implements data governance and privilege of least access practices leveraging security tools.
• Builds responsible AI-compliant data products and/or applications.

Data Management and Transformation

• Plans and creates efficient techniques and operations (e.g., inserting, aggregating, joining) to transform raw data into a form (e.g., dimensional data model) that is compatible with downstream data consumers, databases, and formats that support applications, analytics and reporting.
• Independently uses software, query languages, and computing tools (e.g., cloud-based) to transform raw data across end-to-end pipelines.
• Evaluates data to ensure data quality and completeness using queries, data wrangling, and statistical techniques.
• Merges data into distributed systems, products, or tools for further processing.
• Identifies opportunities to leverage and contribute to the development of data tools that are used to transform, manage, and access data, scaling with efficiency and reduced time to new data insights.
• Writes, implements, and validates code to test storage and availability of data platforms and drives the implementation of sustainable design patterns to make data platforms more usable and robust to failure and change.
• Analyzes relevant data sources that allow others to develop insights into data architecture designs or solution fixes.
• Identifies data sources and builds code to extract raw data from identified upstream sources using query languages, tools, or application programming interfaces (APIs) while ensuring quality, scale, and reliability of the data across several pipeline components.
• Contributes to the code review process by providing feedback and suggestions for…