Senior Lead Data Architect: Information Architecture

JPMorgan Chase & Co.•Plano, TX

About The Position

As a Senior Lead Data Architect, Data Modelling at JPMorganChase within Enterprise Platforms (Employee Platforms) in Corporate Technology, you shape how data is structured, defined, and trusted across products that serve critical employee experiences. You will lead the domain’s data modelling strategy across transactional, analytical, streaming, and AI-driven consumption patterns, ensuring performance, clarity, and safe evolution. You will also help establish semantic foundations so teams and enterprise-authorized AI capabilities can interpret and use data reliably with appropriate validation, security, resiliency, and auditability.

Requirements

Formal training or certification on data architecture and data modelling concepts and 5+ years applied experience.
Extensive hands-on experience designing and delivering optimized data models across raw/document, OLTP 3NF, OLAP dimensional, and event/message schema patterns.
Strong command of core data concepts, including entities, relationships, cardinality, normalization vs. denormalization trade-offs, attribute abstraction, and schema evolution.
Practical experience delivering system design, application development, testing, and operational stability in production environments.
Solid understanding of modern lakehouse and data and analytics platforms, including Databricks (Delta Lake, Unity Catalog, medallion architecture) or equivalent platforms.
Demonstrated experience using enterprise-authorized AI capabilities to support data modelling and architecture workflows, with strong validation habits and awareness of data sensitivity.
Ability to assess and validate AI-assisted modelling recommendations before adoption, escalating uncertainty and ensuring alignment to security, auditability, and resiliency expectations.
Advanced knowledge in one or more programming languages and technical disciplines (e.g., cloud, AI/ML, data platforms), with strong architecture and engineering fundamentals.
Proven ability to independently solve complex data model design and functionality problems with little to no oversight.

Nice To Haves

Deep specialization in data modelling, including the ability to explain and teach model-shape decisions based on consumption patterns.
Experience defining and sustaining a shared business glossary and semantic layer that stays coherent across domains, including for AI-driven consumption.
Strong, current experience optimizing analytical/dimensional models on Databricks or comparable cloud-native data platforms, including performance and cost considerations at scale.
Familiarity with vector and graph modelling and retrieval patterns for AI/ML and agentic consumption, in addition to traditional OLTP and OLAP modelling.
Practical fluency integrating agentic AI workflows into modelling, documentation, and data quality processes, including effective prompting and rigorous validation.
Experience establishing or scaling modelling standards, naming conventions, and governance practices, including onboarding and mentoring other modelers and engineers.
Exposure to schema/contract-driven development (e.g., data contracts, schema registries) and managing schema evolution safely in production.

Responsibilities

Lead the end-to-end data modelling strategy for the domain, aligning logical and physical models to the needs of each product and consumer.
Design and optimize models across raw/denormalized document structures, OLTP relational (3NF), OLAP dimensional (star/snowflake), and event/message schemas.
Define and maintain semantic foundations, including a data dictionary, naming and namespace conventions, and attribute-level abstractions to support interoperability.
Govern schema evolution and model versioning to enable safe change while protecting downstream consumers through clear contracts and lineage expectations.
Represent data architecture and modelling at governance forums, improving standards and guiding technology evaluation against established frameworks.
Provide technical direction and mentorship to engineering teams, contractors, and vendors as a domain subject matter expert in data modelling.
Develop and review secure, high-quality DDL and model-implementing transformation logic, debugging issues and improving production readiness.
Drive modelling decisions that influence product design, application functionality, analytics outcomes, access-pattern performance, and operational stability.
Use enterprise-authorized AI capabilities to accelerate modelling analysis and documentation while validating outputs and aligning to data sensitivity, security controls, resiliency, and auditability expectations.
Champion reuse-first, AI-assisted validation and agentic workflows within the software development lifecycle to strengthen quality checks, documentation, traceability, and model usability for both people and AI systems.