Software Engineer III - Databricks

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