Principal Software Engineer - Data & Analytics Services | Corporate Technology

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.

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  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.