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

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead 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 be entrusted in delivering 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 sound 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. Areas of focus include developer productivity, delivery and portfolio performance, technology spend and value realization, return on investment, and the adoption and impact of Artificial Intelligence across GT.

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.

Job responsibilities:

• Define, create, deliver, establish and maintain a metrics framework and complementary visuals aligned to CTO and technology leadership decision needs.
• Build strong relationships across various GT functions. Communicate statistical findings effectively to technical and non-technical audiences without oversimplification or false precision.
• Work closely to JPMC key strategic programs and initiatives, while providing continuous analysis & insights to support their priority outcomes, all with sound statistical measures.
• Continuously refine analytical approaches as technology strategy, architecture, and delivery practices evolve.
• 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.
• 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.
• Ensure reporting supports informed decision-making rather than metric consumption without context.

Required qualifications, capabilities, and skills:

• Degree in Mathematics, Statistics, Data Science, Engineering, Computer Science or equivalent 5+ years applicable work experience.
• 7+ years 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: Excel, Python, R Studio, Power BI, Tableau, Qlik, SQL, dbt, Databricks, Snowflake, and Microsoft Fabric.
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).

Preferred qualifications, capabilities, and skills:

• 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.
• Proficient in all aspects of the Software Development Life Cycle.
• Proficiency in automation and continuous delivery methods (CI/CD pipelines).