Software Engineer III - Python, PySpark, Databricks, Big Data
Join us to engineer a trusted data platform that helps protect customers and the firm while enabling smarter, faster decisions, you’ll grow your skills alongside experienced engineers, collaborate across teams, and ship high-impact software that scales.
Job summary
As a Software Engineer III at JPMorgan Chase within the Corporate Sector, you serve as a seasoned member of an agile data engineering team, building and delivering a trusted Global Know Your Customer (KYC) and Risk Assessment Data Platform in a secure, stable, and scalable way, you leverage your technical capabilities and collaborate with colleagues across the organization to promote best-in-class outcomes across technologies that support one or more firm portfolios, you help uphold strong engineering practices and deliver high-impact software that scales across multiple teams.
Job responsibilities
- Develop secure, high-quality production code for data-intensive applications and platforms
- Create durable, reusable software frameworks and patterns leveraged across teams and functions
- Advise cross-functional teams on technological matters within your domain of expertise
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve value realized by automation at scale
Required qualifications, capabilities, and skills
- Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale
- Expertise in one or more programming languages, particularly Python and/or Java
- Deep knowledge of software application development and technical processes, with considerable depth in one or more disciplines (for example, cloud, artificial intelligence/machine learning, or data engineering)
- Experience with large-scale data processing, microservices, API design, Kafka, Redis, Memcached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)
- Working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance
- Practical cloud-native experience (AWS, Azure, or GCP)
- Ability to present and effectively communicate with senior leaders and executives
- 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.
Preferred qualifications, capabilities, and skills
- Experience with modern data platforms (e.g., Databricks, Snowflake) and building solutions on cloud-native data ecosystems.
- Strong hands-on big data engineering skills, including Spark/PySpark and related distributed processing technologies.
- Deep expertise in open table formats and metadata/catalog services, such as Apache Iceberg, for scalable, governed data management.
- Experience with large language model (LLM) orchestration frameworks and model serving/managed endpoint infrastructure (e.g., AWS Bedrock, Azure OpenAI).
- Proficient in enterprise-authorized AI-assisted development tools and responsible AI engineering practices—able to validate/refine AI outputs for correctness, performance, and security while guiding peers on safe, compliant usage.
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