Software Engineer III-Databricks

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Category:
Software Engineering

Job Schedule:

Full time

Posted Date: T12:09:47+00:00

Job Shift: Day

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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 product ionizes 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, Sage Maker

* 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, Num Py) and platforms such as Databricks for data engineering and model development at scale

* Experience with ERWIN for data modeling

* Familiarity with Tensor Flow

* Familiarity with data modeling and query optimization