Principal Software Engineer - Data & Analytics Services | Corporate Technology

Company: JPMorganChase
Location: Jersey City
Posted on: April 2, 2026

Job Description:

Description Own high-impact systems that power analytics and AI at scale, and deliver elite performance, security, and cost efficiency. As a Principal Software Engineer at JPMorganChase within Data & Analytics Services within the Corporate Technology team, you will drive the architecture, delivery, and operations of a next-generation, cloud-native distributed data platform. This role owns end-to-end outcomes—partnering with product, data, and infrastructure leaders to deliver reliable, secure, and scalable data services that power analytics, AI/ML, and mission-critical applications. You will set the technical strategy, lead multiple engineering teams, and establish platform standards across compute, storage, streaming, governance, and observability. Job responsibilities Own end-to-end architecture and design for critical platform components across streaming, batch, and interactive workloads; produce ADRs, reference designs, and interfaces making build/buy choices and selecting technologies for storage, compute, streaming, metadata, and orchestration; driving evolution toward lakehouse paradigms. Write high-quality, performant code in Java/Scala/Python/Go; build robust APIs and services; perform deep reviews; establish test strategies and quality gates to implement resilient distributed workflows: exactly-once processing where required, schema evolution, idempotency, backpressure, and failure recovery. Design and optimize compute clusters, storage layers, catalogs, and query engines for elasticity, throughput, and cost efficiency and tune performance across the stack: partitioning, file sizing, caching, vectorization, spill control, and autoscaling policies. Embed IAM/RBAC/ABAC, secrets management, encryption, tokenization, and network controls in services and pipelines to integrate cataloging, lineage, and data quality checks; ensure auditability, retention, and evidence collection in CI/CD and runtime. Define SLIs/SLOs and error budgets for your services; build meaningful metrics, logs, and traces; automate alerts and runbooks to contribute to DR design, multi-region strategies, chaos testing, capacity planning, and incident response/postmortems. Partner with product and platform leads to translate requirements into capabilities and APIs; provide technical leadership without direct people management. Mentor senior engineers, drive design reviews, and champion engineering excellence and risk controls. Required qualifications, capabilities and skills Formal training or certification on software engineering concepts and 10 years applied experience Demonstrable ownership of cloud-native distributed systems or data platforms at scale as a hands-on individual contributor. Experience with Cloud platforms (AWS/Azure/GCP): Kubernetes (EKS/AKS/GKE), serverless, VPC/networking, IAM, and cost optimization. Experience with Storage and lakehouse tech: Object storage (S3/ADLS/GCS), table formats (Delta/Iceberg/Hudi), columnar formats (Parquet/ORC). Data processing/streaming: Spark/Flink/Beam; Kafka/Kinesis/Event Hubs; CDC and schema management. Query/compute engines: Trino/Presto, Snowflake, Databricks, BigQuery; profiling and tuning at TB–PB scale. Strong foundation in distributed systems: consensus, partitioning, replication, consistency models, scheduling, and failure modes. Security and governance experience: encryption, secrets, identity, policy enforcement, DLP, audit logging. DevOps/SRE proficiency: IaC (Terraform/CloudFormation/Bicep), CI/CD, GitOps, blue/green and canary releases, autoscaling and resilience engineering. Excellent system design and communication skills; ability to influence roadmaps and standards across teams without formal authority. Preferred Qualifications Experience in regulated or mission-critical environments with strict RTO/RPO and evidencing requirements. Hands-on with data governance stacks (e.g., Glue/Purview/Data Catalog, OpenLineage), data quality frameworks, and policy engines. Familiarity with ML/AI data patterns: feature stores, model training/inference data pipelines, low-latency serving. Multi-region active-active designs, DR automation, chaos engineering, and capacity modeling. FinOps practices for large-scale data workloads.

Keywords: JPMorganChase, Meriden , Principal Software Engineer - Data & Analytics Services | Corporate Technology, IT / Software / Systems , Jersey City, Connecticut