Sr Principal Data Architect.

Job summary

As a Senior Principal Data Architect at JPMorganChase within the IAM - Data Services Organization, you provide specialized expertise to influence the target state architecture and strategy. Work across teams to implement and advance the adoption of complex, strategic, global data architecture solutions that will give JPMorganChase a competitive advantage. 

You will build a Data Services platform that connects scattered assurance data into one governed model. That model runs from control objectives through to evidence, findings and verified closure. In 2027 it will prove few use cases on a shared data foundation and semantic model before scaling firmwide . We're looking for an architect who has designed, built and operated semantic models for large-scale applications. You will own the enterprise semantic model at the center of this platform.

Job responsibilities

• Own the design of the enterprise semantic model. This means stable entity identities, typed relationships, constraints and versioning across the chain Control Objective → Procedure → Application → Platform → Evidence → Finding → Closure.
• Set and enforce firmwide standards for modelling, identity resolution, taxonomy and ontology. Work with function SMEs (for example IGA and PAM) to register domain taxonomies against a common core.
• Decide when to use graph-native processing and when to use lakehouse/SQL processing, for traversal, impact analysis and graph analytics at scale.
• Design how the semantic layer fits with the data lakehouse (Databricks bronze, silver and gold layers). That includes how gold contracts feed the graph and how lineage and provenance carry through.
• Define data contract and schema evolution patterns so changes never surprise consumers.
• Build point-in-time history into the model so audit evidence packs can be reproduced, and separate configuration evidence from operating evidence.
• Make sure every output carries entitlements, data minimization and quality state (freshness, coverage, completeness, and whether a value was observed, inferred or derived). This applies to dashboards, APIs, data shares and conversational access.
• Design deterministic, auditable context for AI agents, with clear control boundaries and human approval checkpoints.
• Guide producer teams on silver and gold modelling and contract design, and deliver the 2027 proof points.
• Present architecture decisions and trade-offs to senior technology, Risk and Audit stakeholders.

Required qualifications, capabilities, and skills

• A proven record of designing, building and operating semantic models or knowledge graphs for large-scale enterprise applications: many source systems, high-volume entities and relationships, and multiple consuming applications in production.
• Deep expertise in graph and semantic data modelling, covering entity and relationship design, ontology and taxonomy design, identity and entity resolution, constraint design, and temporal or bitemporal (point-in-time) modelling.
• Hands-on experience with graph technologies and query languages, such as a property graph or RDF/OWL. You understand where recursive SQL breaks down for traversal, cycles and graph analytics.
• Strong cloud-native data platform architecture experience, ideally on Databricks or another lakehouse with a bronze/silver/gold (medallion) layout. You can design how a lakehouse and a semantic layer work together.
• Experience setting and running data contracts, schema versioning and change management across many producer teams.
• A proven record of setting data modelling standards across many teams and products, and of influencing senior stakeholders without direct authority.
• Experience with lineage, provenance, data quality and completeness, and entitlement-aware access, including handling sensitive data (for example CCI) at the source.
• Experience leading safe adoption of firm-approved AI capabilities in data architecture workflows, including validation practices and awareness of data sensitivity.
• The ability to evaluate and govern AI-enabled architecture patterns, including agentic workflows, with clear control boundaries, auditability and human approval checkpoints aligned to resiliency and security expectations.
• The ability to evaluate current and emerging technologies and recommend data architecture approaches that reach the target architecture.

Preferred qualifications, capabilities, and skills

• Experience modelling controls, risk, audit or compliance data, such as control-to-evidence mapping, findings and remediation workflows, or continuous control monitoring.
• Familiarity with identity and access management data (IGA, PAM, entitlements, privileged access).
• Experience with metadata management, data catalogues, MDM, and lineage or provenance at enterprise scale.
• AI/ML enablement through graphs (feature generation, embeddings, relationship-aware retrieval or GraphRAG), with strong grounding in data controls.
• Experience building self-service data platforms (YAML/SQL-declared pipelines, automated deploys, guardrails, quotas and budgets).
• Experience at a regulated financial institution.

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