Sr. Principal Software Engineer

We’re looking for a tech leader ready to focus on Data governance, authorization, and data-mesh architecture — with regional technical leadership across the India team
As a Senior Principal Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets - Markets Data Lake (MDL) team, you will be the technical authority for how data is governed, secured, and authorized across this mesh, and you will provide regional technical leadership across the India team — one of MDL's primary engineering hubs. You will set direction, grow senior talent locally, and act as the senior technical anchor in-region, while staying hands-on with the platform's most critical governance and authorization components.
Job responsibilities
Serve as the senior technical leader in-region for the India-based MDL engineering team, setting technical direction and ensuring alignment with the global platform roadmap. Grow and mentor senior, staff, and up-and-coming engineers in India; establish a strong local engineering culture, review practices, and career-development pathways.
Own regional delivery of major platform initiatives end-to-end, coordinating across time zones with global stakeholders and partner teams. Build in-region depth in governance, authorization, and data-mesh capabilities so the India team owns critical platform domains, not just execution.
Represent MDL locally — interviewing and hiring, onboarding, and raising the engineering bar across the India hub. Bridge global and regional priorities, ensuring the India team's work is visible, influential, and tightly integrated with the broader organization.
Architect the data-mesh platform: define what a "data product" is on MDL — its contracts, SLAs, schemas, ownership, lifecycle, and interoperability standards — and build the self-serve platform capabilities domain teams use to publish them. Establish federated computational governance: design the policies-as-code framework that lets central standards (security, quality, entitlements, retention) be enforced automatically across independently-owned domains.
Build the data-product substrate: catalog, discovery, lineage, versioning, and distribution across the platform's query engines, so consumers can find and query products through the semantic layer and NL-query interfaces without bypassing controls.
Keep the architecture engine-agnostic — design governance, entitlements, and data-product contracts to work uniformly across current and future storage and compute engines rather than being tied to any single technology. give domains freedom to model and evolve their products while guaranteeing platform-wide consistency and safety.
Own the governance model end-to-end: data classification, metadata management, lineage, data quality, retention, and audit across every MDL data product. Codify governance as code — schema/contract validation, quality gates, and policy checks embedded in publishing and CI/CD pipelines so governance is enforced, not advisory. Drive lineage and auditability: every query and every distributed dataset must be traceable, correlated, and reproducible for regulatory and internal audit needs.
Partner with data owners, risk, compliance, and privacy to translate regulatory and firm obligations into automated platform controls. Own the authorization architecture for MDL: fine-grained, row- and column-level entitlements enforced consistently across every query engine and the query/agent layer (nl_query/execute_query).
Design a scalable entitlements model — attribute/role/policy-based access control, entitlement propagation from source systems, and least-privilege by default. Guarantee "no query bypasses entitlements": ensure NL-generated and raw SQL alike are planned and executed within the caller's entitlement scope, with credentials brokered securely.
Harden identity and access: integration with enterprise IdP/OAuth2, token lifecycle, secrets handling, and full access audit trails. Set technical direction across multiple teams via design docs, RFCs, and architecture reviews focused on governance, security, and mesh interoperability. Stay hands-on — personally build reference implementations for the highest-risk governance/authorization components and set the pattern others follow. Mentor and grow senior and staff engineers globally and in-region; establish best practices for secure-by-design data engineering.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 10+ years applied experience
Software engineering experience with significant time architecting large-scale data platforms.
Demonstrated regional/site technical leadership — anchoring a distributed engineering team (ideally in India), growing senior talent, and delivering across time zones.
Deep expertise in data governance and authorization at scale: fine-grained entitlements (row/column-level security), ABAC/RBAC/policy-based access control, data classification, lineage, and audit.
Demonstrated experience designing and scaling agentic AI-enabled development patterns (using enterprise-authorized tools within the work environment) across teams/functions, including establishing governance for human-in-the-loop validation, traceability/auditability, and secure handling of sensitive inputs/outputs.
Strong understanding of responsible AI use and control expectations at scale, including security/resiliency implications, data sensitivity, and risk-based governance; ability to advise senior leaders on safe adoption, reuse, and measurable outcomes.
Strong experience designing secure, governed data platforms on the cloud, including identity/OAuth2 token-based authorization — applied in an engine-agnostic way rather than tied to a single warehouse or lake technology.
Data-mesh or federated data-platform experience — designing data products, data contracts, and federated/computational governance.
Strong SQL and data modeling; experience with a semantic/metrics layer.
Proficiency in a primary backend language (Python, Java, Go, or similar) and policy-as-code tooling.
Excellent cross-time-zone communication, design-doc, and stakeholder-management skills.
Preferred qualifications, capabilities, and skills
Capital markets / financial services data experience (FX, rates, credit, equities, reference data) and familiarity with the associated regulatory and data-privacy landscape.
Experience building and scaling engineering teams within a global delivery / India hub model.
Experience with data catalogs, lineage, and governance tooling (e.g. OpenLineage/DataHub, Collibra, Immuta/Okera-style policy engines).
Experience securing natural-language / LLM-agent data access (MCP) — ensuring generated SQL is entitlement-safe and deterministic.
Familiarity with a range of storage/compute engines and lakehouse formats (e.g. Iceberg, Delta, Hudi) and streaming/batch pipelines (Kafka, Spark, Flink), with the judgment to choose the right tool per use case.
Infrastructure-as-code (Terraform) and mature CI/CD with embedded policy gates.