Lead Data Analytics Engineer

Job Summary

As a Lead Data Analytics Engineer at JPMorganChase within the Global Technology - Analytics, Insights and Measurements (GT AIM) team, you will deliver trusted, decision-grade insight across Global Technology through rigorous statistical analysis and domain-informed interpretation. This role offers an opportunity to impact careers by pushing the limits of what's possible, contributing to market-leading technology products in a secure, stable, and scalable way. You will serve as a core technical contributor, 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), the Lead Data Analytics Engineer applies sound statistical and analytical methods to technology data to inform strategy, execution, and investment decisions across multiple technology domains. The role involves close partnership with leaders of strategic programs, providing continuous statistical analysis and insight to support priority outcomes.

A deep understanding of software engineering delivery models and flows (e.g., feature branch, trunk-based, integrated delivery) is essential to ensure metrics and analysis accurately reflect technology delivery. Key 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 Global Technology.

The emphasis is on building internally owned, transparent, and explainable analytics through sound statistical methods, prioritizing clarity over opaque third-party tools. All roles are hands-on, with managers providing leadership and direction while actively contributing to analysis and insight delivery. Senior Individual Contributors independently own complex analytical problems and influence outcomes through their expertise and insight.

Job Responsibilities

Insights, Communications and Reporting

• Define, create, deliver, establish, and maintain a metrics framework and complementary visuals aligned to CTO and technology leadership decision needs. This framework will encompass various technology initiatives, including emerging capabilities like Artificial Intelligence (AI), Software Engineering, and Portfolio Management.
• Build strong relationships across various GT functions and communicate statistical findings effectively to technical and non-technical audiences without oversimplification or false precision. Narratives and analyses must be clear, articulating what is happening, why it is happening, and the confidence level of conclusions.
• Work closely with JPMC’s key strategic programs and initiatives, providing continuous analysis and insights with sound statistical measures to support their priority outcomes. Insights must explain performance, trends, variability, and drivers across all of Global Technology.

Statistical Analysis and Data Interpretation

• Continuously refine analytical approaches as technology strategy, architecture, and delivery practices evolve. Support technology leadership in understanding trade-offs, risks, opportunities, and uncertainty.
• Ensure conclusions are 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 these techniques and reasoning to assess variability, confidence, uncertainty, statistical significance, and margin of error where appropriate.
• Evaluate distributions, trends, and changes over time, accounting for structural differences in teams, systems, and delivery models. Clearly distinguish correlation from causation and communicate analytical limitations, assumptions, and confidence levels.

Operations, Measurements and Instrumentation

• 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 mere 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, with 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 including Excel, Python, R Studio, Power BI, Tableau, Qlik, SQL, dbt, Databricks, Snowflake, and Microsoft Fabric, alongside specialized portfolio and 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, etc.).

Preferred Qualifications, Capabilities, And Skills

• Desire and ability to mentor peers through statistical expertise and engineering domain knowledge.
• Strong formal training in statistics and a commitment to statistical rigor.
• Intellectual curiosity and 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).
• Practical understanding of software engineering delivery models, including feature branch, trunk-based, and integrated delivery.

Key skills/competency

• Statistical Analysis
• Data Interpretation
• Metrics Framework Design
• AI Analytics
• Software Engineering Delivery Models
• Data Instrumentation
• Stakeholder Communication
• Python/R Programming
• SQL Expertise
• Modern Data Stack