Job description

Embrace this pivotal role as an essential member of a high performing team dedicated to reaching new heights in data engineering. Your contributions will be instrumental in shaping the future of one of the world’s largest and most influential companies. As a Senior Lead Data Engineer at JPMorganChase within the Employee Data Technology, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics in a secure, stable, and scalable way. Leverage your deep technical expertise and problem solving capabilities to drive significant business impact and tackle a diverse array of challenges that span multiple data pipelines, data architectures, and other data consumers. Job responsibilities • Provides recommendations and insight on data management and governance procedures and intricacies applicable to the acquisition, maintenance, validation, and utilization of data • Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and design analysis and technical documentation, validating outputs and handling data according to sensitivity and security requirements. • Designs and delivers trusted data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way • Defines database back-up, recovery, and archiving strategy • Generates advanced data models for one or more teams using firmwide approaches, linear algebra, statistical and geometrical algorithms • Approves data analysis tools and processes • Creates functional and technical documentation supporting best practices • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team • 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 • Evaluates and reports on access control processes to determine effectiveness of data asset security • Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., validation automation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations. Required qualifications, capabilities, and skills • Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience • Hands-on practical experience delivering system design, application development, testing, and operational stability • Strong Python coding skills, and deep experience with Databricks and Spark. • Experience with both relational and NoSQL databases • Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity. • Ability to review and validate AI-assisted outputs (e.g., model and design summaries or validation recommendations) before use, escalating when uncertain and following data handling requirements. • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices • Strong experience with AWS and modern lakehouse and data platform patterns. • Proven technical leadership through architecture decisions, mentoring, and cross-team influence. Preferred qualifications, capabilities, and skills • AWS certification and/or Databricks certification. • Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery. Drive business impact and tackle data engineering challenges that span multiple data pipelines, architectures, and consumers

Requirements

Must-haves

Provides recommendations and insight on data management and governance procedures and intricacies applicable to the acquisition, maintenance, validation, and utilization of data
Designs and delivers trusted data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
Defines database back-up, recovery, and archiving strategy
Generates advanced data models for one or more teams using firmwide approaches, linear algebra, statistical and geometrical algorithms
Approves data analysis tools and processes
Creates functional and technical documentation supporting best practices
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
Evaluates and reports on access control processes to determine effectiveness of data asset security
Required qualifications, capabilities, and skills
Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Strong Python coding skills, and deep experience with Databricks and Spark.
Experience with both relational and NoSQL databases
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs (e.g., model and design summaries or validation recommendations) before use, escalating when uncertain and following data handling requirements.
Strong experience with AWS and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.
AWS certification and/or Databricks certification.
Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.
Drive business impact and tackle data engineering challenges that span multiple data pipelines, architectures, and consumers

Nice-to-haves

Preferred qualifications, capabilities, and skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

agileaipythonaws

Platform: oracle-cloud
Effort: medium