Senior Lead Data Engineer - Financial Accounting

Testing Site - JPMC; Columbus (OH); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorgan Chase in Columbus is seeking a Senior Lead Data Engineer to drive data engineering within Enterprise Technology, focusing on the General Ledger data strategy and cross-functional collaboration with software engineers and platform teams. You will deliver secure, scalable data products and guide junior engineers in data management, governance, and tooling adoption.

This role requires cloud data engineering expertise, strong programming skills, and hands-on experience with Databricks, AWS,

Qualifications

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience in cloud data engineering and platform engineering.
• Strong expertise in AWS (VPC design, subnets, routing, security groups, NACLs).
• Proficiency in Kubernetes (EKS), AWS ECS, containerization, orchestration, and workload scaling.
• Advanced programming skills in Java/Python and SQL
• Hands-on experience with Databricks (Unity Catalog, Delta Lake) and familiarity with Lakehouse patterns.
• Advanced understanding of agile methodologies, CI/CD, application resiliency, and security.
• Proficiency in automation and continuous delivery methods (CI/CD, IaC with Terraform or CloudFormation), automated testing, and release pipelines.
• Working experience with both relational and NoSQL databases like MongoDB.
• Advanced understanding of database backup, recovery, and archiving strategy.
• Experience presenting and delivering visual data to senior stakeholders.

Responsibilities

• Partner with engineering and central platform teams to enable and scale core data lake capabilities on Databricks, covering ingestion, processing, governance, cataloging, quality, lineage, access, and consumption.
• Pilot and evaluate new tools and services; run proof-of-concepts, define selection criteria and success metrics, and integrate successful solutions into the platform.
• Operate within Agile SDLC; coordinate with stakeholders and provide clear updates on progress and priorities.
• Drive performance, scalability, and reliability through capacity planning, stress testing, and performance tuning.
• Document best practices, guardrails, patterns, and runbooks; maintain a knowledge base and onboarding materials for the broader engineering community.
• Explore and implement agentic solutions and automation tools to improve developer experience and operational efficiency.
• Provide recommendations and insight on data management and governance procedures, including acquisition, maintenance, validation, and utilization of data.
• Design and deliver secure, stable, and scalable data platform solutions, including database backup, recovery, and archiving strategies.
• Approve data analysis tools and processes, generate advanced data models, and advise junior engineers and technologists.
• Evaluate and report on access control processes to ensure effectiveness of data asset security.

Skills

AWS data engineering

SQL

Java/Python

Agile methodologies

Data governance

Stakeholder communication

Tools

Databricks

Unity Catalog

Delta Lake

Terraform

CloudFormation

Kubernetes (EKS)

AWS ECS

MongoDB

CI/CD pipelines

Job description

You have the opportunity to unleash your full potential at a world-renowned company and take the lead in shaping the future of technology.

As a Senior Lead Data Engineer at JPMorgan Chase within Enterprise Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. Lead data engineering within Finance Technology for the General Ledger, defining the data strategy and partnering with software engineers and central platform teams to deliver world class data capabilities.

Job responsibilities

• Partner with engineering and central platform teams to enable and scale core data lake capabilities on Databricks, covering ingestion, processing, governance, cataloging, quality, lineage, access, and consumption.
• Pilot and evaluate new tools and services; run proof-of-concepts, define selection criteria and success metrics, and integrate successful solutions into the platform.
• Operate within Agile SDLC; coordinate with stakeholders and provide clear updates on progress and priorities.
• Drive performance, scalability, and reliability through capacity planning, stress testing, and performance tuning.
• Document best practices, guardrails, patterns, and runbooks; maintain a knowledge base and onboarding materials for the broader engineering community.
• Explore and implement agentic solutions and automation tools to improve developer experience and operational efficiency.
• Provide recommendations and insight on data management and governance procedures, including acquisition, maintenance, validation, and utilization of data.
• Design and deliver secure, stable, and scalable data platform solutions, including database backup, recovery, and archiving strategies.
• Approve data analysis tools and processes, generate advanced data models, and advise junior engineers and technologists.
• Evaluate and report on access control processes to ensure effectiveness of data asset security.

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience in cloud data engineering and platform engineering, with a focus on enabling data platform capabilities.
• Strong expertise in AWS (VPC design, subnets, routing, security groups, NACLs).
• Proficiency in Kubernetes (EKS), AWS ECS, containerization, orchestration, and workload scaling.
• Advanced programming skills in Java/Python and SQL
• Hands-on experience with Databricks (Unity Catalog, Delta Lake) and familiarity with Lakehouse patterns.
• Advanced understanding of agile methodologies, CI/CD, application resiliency, and security.
• Proficiency in automation and continuous delivery methods (CI/CD, Infrastructure as Code with Terraform or CloudFormation), automated testing, and release pipelines.
• Working experience with both relational and NoSQL databases like MongoDB.
• Advanced understanding of database backup, recovery, and archiving strategy.
• Experience presenting and delivering visual data to senior stakeholders.

Preferred qualifications, capabilities, and skills

• Hands-on experience in cloud data engineering and platform engineering, with a focus on enabling data platform capabilities.