Job description

If you're energized by building platforms at scale, driving transformation, and seeing your work influence an entire enterprise, this role offers the technical depth, strategic ownership, and career growth that few opportunities can match. This is a highly visible leadership role where you'll work with cutting-edge technologies including Databricks, AWS, Spark, and modern Lakehouse architectures while solving complex, large-scale engineering challenges. Beyond the technology, you'll have the opportunity to collaborate directly with senior leaders, mentor talented engineering teams, and leave a lasting impact on how data is managed, governed, and consumed across the organization. As a Senior Lead Software Engineer at JPMorganChase within Corporate Risk Technology, you will lead the architecture and delivery of an enterprise-scale data platform that enables trusted risk data products in a secure, stable, and scalable way. 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. Drive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications

Requirements

Must-haves

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.
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.
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.
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.

Nice-to-haves

Preferred qualifications, capabilities, and skills

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Keywords to mirror

awsci/cdaipythonjavasqlapi

Platform: oracle-cloud
Effort: medium

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