Principal Software Engineer - Environments Engineering

JPMorgan Chase & Co.•Palo Alto, CA

9d•$204,250 - $285,000•Hybrid

About The Position

As a Principal Engineer at JPMorganChase within the Commercial and Investment Bank Digital Channels and Connectivity team, you will own the technical strategy and delivery of the firm's next-generation test environment platform — enabling every developer across a 10,000-engineer organization to spin up an isolated, composable, production-representative virtual environment per pull request, run integration tests independently and simultaneously, and merge with confidence without contention on shared environments. You will solve the fundamental constraint that limits flow of code to production: environment bottlenecks. Your approach will be composable — reusing most unchanged components while instantiating only the services under test, with intelligent dependency resolution, traffic isolation, and automatic teardown. You will design and build the MVP hands-on, then shift to hardening, socializing, and driving adoption at scale across multiple organizations. You will partner as a peer to engineering delivery leadership and collaborate closely with performance engineering, SRE, and quality engineering to ensure the platform meets the demands of a large, hybrid (cloud and on-premises) service estate.

Requirements

15+ years of professional software engineering experience, with significant experience building internal developer platforms, environment-as-a-service systems, or large-scale infrastructure orchestration consumed by thousands of engineers
Demonstrated experience designing composable or virtual environment architectures — including service graph decomposition, selective instantiation, dependency stubbing/virtualization, and traffic isolation patterns that avoid full-clone approaches
Deep expertise in traffic routing for environment isolation — header-based routing, service mesh traffic control (Istio, Linkerd, or equivalent), service discovery, and strategies that prevent cross-environment contamination at high concurrency
Production experience operating Kubernetes across on-premises and cloud environments (EKS), with strong understanding of namespace isolation, resource scheduling, multi-tenancy, and operational tradeoffs across hybrid infrastructure
Hands-on proficiency in Go, Rust, or Java for building production-grade platform services, combined with strong infrastructure-as-code experience (Terraform, Ansible) for provisioning and configuration management with change control and repeatability
Proven ability to provision and manage stateful test infrastructure in an automated fashion across heterogeneous data stores — relational databases (Oracle, PostgreSQL), document stores (MongoDB, CockroachDB), message brokers (Kafka), and cloud-native services (DynamoDB)
Experience with CI/CD pipeline design and extension — integrating environment lifecycle as a pipeline-native capability across Jenkins, Spinnaker, GitHub Actions, or Argo CD
Strong platform-as-a-product thinking — designing clean APIs, contracts, composability, multi-tenancy, scheduling, cost modeling, and measuring platform success through adoption metrics and developer experience outcomes (DORA, time-to-environment, satisfaction)
Demonstrated ability to drive platform adoption at scale (1,000+ engineers) through advocacy, enablement, excellent self-service UX, and partnership with engineering leadership — not just building but ensuring the organization actually uses it
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse

Nice To Haves

Namespace-per-PR / lightweight isolation (no full env cloning); GitOps environment-as-code alongside app code
Scalable test data: masked prod snapshots, on-demand restores, synthetic data, repeatable dataset management across platforms
Developer portal integration (Backstage or equivalent) for self-service, catalog visibility, and env health dashboards
AI/ML for infra optimization: predictive scaling, change-set–driven env composition, automated dependency graph resolution
Evaluate/integrate open source for enterprise use: supportability, security, supply-chain integrity, long-term viability
Hybrid connectivity expertise: on-prem ↔ cloud routing, VPN/private link topologies, latency considerations for ephemeral envs
Financial-services regulated operations: compliance, data sensitivity controls, auditability as hard constraints

Responsibilities

Architect the composable virtual environment platform — enabling per-PR, per-developer isolated test sessions that reuse unchanged shared services and instantiate only the components under test/change, with correct dependency resolution across the service graph
Design the traffic routing and isolation layer that directs requests to the correct version of each service within a virtual environment session, leveraging header-based routing, service mesh capabilities, and service discovery without cross-session contamination
Build the dependency graph intelligence that determines which services must be instantiated live vs. reused from baseline vs. virtualized/stubbed for a given test scenario — making composable environments feasible at 1,000+ service scale
Deliver a self-service developer experience through CLI, API, and developer portal integration that enables teams to declare environment needs, provision in minutes, execute integration tests, and exit — with zero dependency on a shared environment or manual coordination
Define and implement data strategies for composable environments, including synthetic data generation, production data masking, snapshot/restore workflows, schema versioning, and repeatable dataset management across heterogeneous data stores (Oracle, Kafka, Cassandra, MongoDB, CockroachDB, DynamoDB, PostgreSQL)
Implement lifecycle management, cost governance, and resource reclamation — including time-to-live policies, idle detection, automatic teardown, quota management, chargeback attribution, and FinOps reporting across cloud and on-premises infrastructure
Integrate the platform into CI/CD pipelines as a native stage — environments created on PR open, tests executed, results reported, environments destroyed on merge/close — with support for Jenkins, Spinnaker, GitHub Actions, and Argo CD
Ensure the platform operates reliably across hybrid infrastructure (Kubernetes on-premises and EKS, VMs, and managed cloud services) with consistent provisioning semantics, cross-network connectivity, and acceptable spin-up latency regardless of hosting tier
Establish platform reliability SLOs, observability, and operational runbooks — measuring time-to-environment, environment success rate, isolation correctness, and developer satisfaction as first-class product metrics
Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale