Software Engineer III - Databricks

JPMorgan Chase & Co.; Wilmington (DE); On-site; USD 135,000 - 195,000; Full time

Job summary

JPMorgan Chase & Co. invites a seasoned Software Engineer III to join the Databricks-focused Enterprise Technology team in the Corporate Sector.

You will design and deliver scalable, secure technology products, leading multi-domain solutions and data pipelines while advancing ML model deployment in production environments. The role emphasizes strong Python skills, Databricks expertise, and collaboration with Data Science and SRE/Platform teams to ensure resiliency, observability, and secure

Qualifications

• Formal training or certification on software engineering concepts and 3+ years applied experience.
• Hands‑on experience in software engineering, system design, application development, testing, and operational stability.
• Proficiency in Python; strong grounding in secure data practices.

Responsibilities

• Provides technical leadership across design, development, and troubleshooting for complex, multi-domain solutions; establish engineering standards and best practices for the team.
• Writes secure, high-quality code in Python and/or Java; conducts reviews and mentors engineers to raise code quality and maintainability.
• Builds data pipelines using Databricks ETL.
• Builds and productionizes cloud-based ML pipelines; drive model deployment and operationalization in collaboration with Data Science and SRE/Platform teams.
• Owns MLOps workflows; coordinates infrastructure and production changes with SRE; ensures resiliency, observability, and security across the ML lifecycle.
• Applies SDLC tooling and automation to improve delivery velocity and reliability; champion CI/CD and cloud-native best practices.
• Partners with Product Owners and business stakeholders to translate requirements into scalable solutions aligned to CCB Finance objectives.
• Fosters a team culture of diversity, opportunity, inclusion, and respect; model proactive learning in AI/ML and emerging technologies.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables.

Skills

Python

Secure data practices

CI/CD

Agile/Scrum

SDLC automation

Databricks

AWS (ECS/EMR/Lambda/EC2/SageMaker)

Kubernetes

PySpark

Kafka

Tools

Databricks Delta Lake

Unity Catalog

Workflows

Repos/notebooks

SQL Warehouses

Terraform

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III - Databricks at JPMorgan Chase within the Corporate Sector's Enterprise Technology team, 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. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Provides technical leadership across design, development, and troubleshooting for complex, multi-domain solutions; establish engineering standards and best practices for the team
• Writes secure, high-quality code in Python and/or Java; conducts reviews and mentors engineers to raise code quality and maintainability
• Builds data pipelines using Databricks ETL
• Builds and productionizes cloud-based ML pipelines; drive model deployment and operationalization in collaboration with Data Science and SRE/Platform teams
• Owns MLOps workflows; coordinates infrastructure and production changes with SRE; ensures resiliency, observability, and security across the ML lifecycle
• Applies SDLC tooling and automation to improve delivery velocity and reliability; champion CI/CD and cloud-native best practices
• Partners with Product Owners and business stakeholders to translate requirements into scalable solutions aligned to CCB Finance objectives
• Fosters a team culture of diversity, opportunity, inclusion, and respect; model proactive learning in AI/ML and emerging technologies
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness
• 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

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Hands‑on experience in software engineering, system design, application development, testing, and operational stability
• Proficiency in Python; strong grounding in secure data practices
• Hands‑on Databricks experience across Delta Lake, Unity Catalog, Workflows, Repos/notebooks, and SQL Warehouses, including cluster configuration and optimization
• Cloud engineering experience building ML pipelines and deploying models to production with AWS services such as ECS, EMR, Lambda, EC2, SageMaker
• Experience with PySpark, Kafka, Terraform, and Kubernetes for data processing, streaming, IaC, and container orchestration
• Database experience with Oracle and/or Cassandra
• Familiarity with CI/CD, application resiliency, security best practices, Agile/Scrum methodologies, and SDLC automation tools
• Hands‑on experience using enterprise-authorized AI‑assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI‑generated outputs for correctness, performance, and security
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred qualifications, capabilities, and skills

• Background with machine learning frameworks, MLOps practices, and end‑to‑end ML lifecycle management (feature pipelines, model registry, monitoring, drift detection)
• Experience with the Python ML ecosystem (pandas, NumPy) and platforms such as Databricks for data engineering and model development at scale
• Experience with ERWIN for data modeling
• Familiarity with TensorFlow
• Familiarity with data modeling and query optimization