Software Engineer III-Databricks

JPMorgan Chase & Co.•Wilmington, DE

13d

About The Position

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.

Requirements

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

Nice To Haves

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

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