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Senior Lead Software Engineer - Risk Technology Data Platform & Strategy

JPMorgan Chase
Jersey City, USA
Full-time
Senior · 5+ years experience
Onsite
Discovered 2 weeks ago
awsdatabricksdelta-lakeicebergjavakafka

Full Job Posting

Job responsibilities

  • Owns the technical direction of the platform and lead architecture and design leveraging a Lakehouse on Databricks and AWS, including multi-tenant patterns, cataloging, storage formats (e.g., Delta Lake / Apache Iceberg), and data product publishing.
  • Delivers secure, production-grade code for data-intensive systems, including ingestion, transformation, and serving pipelines across batch, streaming, and change data capture (CDC).
  • Scales tenant onboarding through self-service by automating onboarding, schema/catalog provisioning, and pipeline orchestration.
  • Build infrastructure-as-code and deployment pipelines using Terraform (or equivalent), CI/CD automation, and preventative controls/guardrails aligned to firm policy.
  • Makes data governance, entitlements, and data protection integral to the platform design, including least-privilege access controls, approval workflows, and auditability.
  • Ensures resilience, observability, and operational excellence by defining SLOs/SLAs, instrumenting services/pipelines (metrics/logs/traces), and leading incident and root-cause analysis in partnership with SRE.
  • Acts as a subject-matter expert for risk data engineering and guide teams on architecture trade-offs, data quality, lineage, and reproducibility.
  • Leads modernization and technology evaluation, balancing innovation with cost, security, supportability, and operational readiness.
  • Drives adoption of enterprise-authorized AI-assisted engineering practices (e.g., code review/refactoring support, test acceleration, troubleshooting support) with clear validation expectations for correctness, performance, and security.
  • Influences senior stakeholders across Risk and Technology by aligning roadmaps, prioritizing feature requests, and resolving control findings.
  • Builds and lead a high-performing engineering culture through mentoring, strong engineering practices, and a culture of ownership and inclusion.

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 5+ years of applied experience.
  • Hands-on architecture and delivery of large-scale enterprise data platforms (multi-tenant lakehouse or data mesh), including system design, development, testing, and operational stability.
  • Advanced proficiency in Python and/or Java (or Scala) and SQL, with a track record of maintaining production systems in a regulated environment.
  • Deep experience with distributed processing and lakehouse technologies: Apache Spark, Databricks (including Unity Catalog), and open table formats (Delta Lake and/or Apache Iceberg).
  • Experience with data ingestion and integration at scale, including CDC from relational sources, event streaming (e.g., Kafka/Kinesis), file/API ingestion, and schema evolution / data contracts.
  • Practical cloud-native experience, AWS (e.g., S3, IAM, KMS, container platforms, and analytics services), plus infrastructure-as-code and CI/CD in a policy-governed environment.
  • Strong data governance and security expertise: fine-grained access controls, entitlement/approval workflows, data classification, lineage/data quality, and experience supporting data residency/localization and sensitive data handling obligations.
  • Platform reliability and operations: microservices and API design, orchestration tooling, observability, SLO/SLA definition, incident management, and root-cause analysis.
  • Proven technical leadership: driving architecture decisions through structured review, influencing senior stakeholders, and mentoring senior engineers and leads.
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools, with the ability to set expectations for validating AI outputs.
  • Strong understanding of responsible AI use in engineering workflows, including secure handling of inputs/outputs and data sensitivity considerations.

Preferred qualifications, capabilities, and skills

  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Mathematics, or a related technical field.
  • Knowledge of financial services risk management data (e.g., credit, market, liquidity, country, model risk) and regulatory reporting / lineage expectations.
  • Experience with AWS-native analytics/data services and integrating Databricks within an AWS-governed data estate.
  • Experience building self-service data platform capabilities: automated onboarding, provisioning control planes, data product catalogs/publishing, and governed data sharing.

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