Principal Data Analytics Engineer - Global Technology Analytics, Insights and Metrics

As a Principal Data Analytics Engineer at JPMorgan Chase within the Global Technology - Analytics, Insights and Measurements (GT AIM) team, you will deliver trusted, decision-grade insight across GT through rigorous statistical analysis and domain-informed interpretation. You will deliver market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm\'s business objectives. Reporting to the Head of GT Architecture and Strategy (GTAS), this role applies statistical and analytical methods to technology data to inform strategy, execution, and investment decisions across multiple technology domains. The role works in close partnership with leaders of strategic programs, providing continuous statistical analysis and insight to support priority outcomes. The role requires deep understanding of software engineering delivery models and flows (e.g., feature branch, trunk-based, and integrated delivery) to ensure metrics and analysis accurately reflect how technology is delivered. The emphasis is on building internally owned, transparent, and explainable analytics through sound statistical methods, rather than relying on opaque third-party tools. All roles are hands-on. Managers provide leadership and direction while actively contributing to analysis and insight delivery. Senior Individual Contributors independently own complex analytical problems and influence outcomes through expertise and insight.

• Define, create, deliver, establish and maintain a metrics framework and complementary visuals aligned to CTO and technology leadership decision needs. Your framework will be inclusive of many different technology initiatives, including emerging capabilities such as Artificial Intelligence (AI), Software Engineering, Portfolio Management and more.
• Build strong relationships across various GT functions. Communicate statistical findings effectively to technical and non-technical audiences without oversimplification or false precision. Narratives and analyses need to be clear. They need to articulate what is happening, why it is happening, and how confident the conclusions are.
• Work closely with JPMC key strategic programs and initiatives, while providing continuous analysis & insights to support their priority outcomes, all with sound statistical measures. Your insights must explain performance, trends, variability, and drivers across all of GT.
• Lead, coach and develop a small team of highly skilled analytics professionals. Manage corresponding standards for statistical rigor, transparency and clarity.
• Continuously refine analytical approaches as technology strategy, architecture, and delivery practices evolve. Support technology leadership in understanding trade-offs, risks, opportunities, and uncertainty.
• Conclusions provided must be sound, statistically and contextually valid and based on actual engineering and business ecosystems. Collaborate closely with engineering, platform, architecture, and AI enablement teams to understand delivery practices, workflows and constraints.
• Perform hands-on statistical analysis using appropriate descriptive, inferential, and exploratory techniques. Apply those techniques and reasoning to assess variability, confidence, uncertainty, statistical significance, and margin of error where appropriate.
• Evaluate distributions, trends, and changes over time while accounting for structural differences in teams, systems, and delivery models. Distinguish correlation from causation and clearly communicate analytical limitations, assumptions, and confidence levels.
• Identify required data points needed to answer key analytical and statistical questions, then define requirements for instrumenting data at the source.
• Ensure metrics are compatible with different engineering flows, including feature branch development, trunk-based development, and integrated delivery.
• Improve data quality, consistency, and traceability over time. Maintain clear documentation of metric definitions, statistical methods, and calculation logic.
• Ensure reporting supports informed decision-making rather than metric consumption without context.

• Degree in Mathematics, Statistics, Data Science, Engineering, Computer Science or equivalent with 7+ years of applicable work experience.
• 10+ years of experience performing statistical analytics, data science, or performance measurement roles.
• Practical experience working with technology, delivery, portfolio, financial, or AI-related data.
• Demonstrated experience applying statistical methods to real-world, imperfect datasets and evolving delivery practices.
• Strong familiarity with concepts such as statistical significance, confidence intervals, variability, and margin of error, and when their use is appropriate.
• Proficiencies in a modern data stack (e.g., Excel, Python, R Studio, Power BI, Tableau, Qlik, SQL, dbt, Databricks, Snowflake, Microsoft Fabric) and portfolio/spend analytics tools like Apptio.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile).
• Experience influencing senior technology leaders and guiding decision-making.

• Desire and ability to mentor peers through statistical expertise and engineering domain knowledge.
• Strong formal training in statistics.
• Intellectual curiosity and commitment to statistical rigor.
• Respect for the complexity and variability of software delivery systems within a large enterprise.
• Practical cloud native experience.
• Proficiency in automation and continuous delivery methods (CI/CD pipelines).
• Practical understanding of software engineering delivery models, including but not limited to feature branch, trunk-based, and integrated delivery.
• Experience leading or mentoring analytics professionals.