Software Engineer III - Data/Payments Technology

JPMorganChase•Plano, TX

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Commercial & Investment Bank - Sales Enablement Data 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. As an Associate Engineer, you will contribute to building and maintaining scalable data platforms that support a wide range of business and operational needs. You will work across diverse data ecosystems, including Big Data technologies such as Databricks, Snowflake, and Iceberg, as well as traditional relational databases like PostgreSQL and Oracle. This role requires a flexible approach to data engineering, software engineering supporting both analytical and operational workloads with a focus on data quality, performance, and reliability.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on experience or familiarity with Big Data ecosystems (Databricks, Snowflake, Iceberg) and relational/NoSQL databases.
Proficiency in programming languages such as Python, Java and familiarity with cloud platforms (AWS, Azure, GCP).
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.
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Overall knowledge of the Software Development Life Cycle
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Knowledge of data modeling, ETL/ELT processes, data warehousing, and security/compliance best practices.
Awareness of security best practices and compliance requirements in data handling.
Strong problem-solving skills and ability to work independently and collaboratively.

Nice To Haves

Experience with big data and streaming technologies (Hadoop, Kafka), containerization/orchestration tools (Docker, Kubernetes), and observability tools.
Certification in cloud data engineering (e.g., AWS Certified Data Engineer Associate).
Exposure to AI/ML platforms, data science workflows, and financial services or regulated industries.
Bachelor’s degree in Computer Science, Engineering, Information Technology, or related field.

Responsibilities

Develop, optimize, and maintain data pipelines and ETL processes for large-scale data ingestion, transformation, and integration across cloud and hybrid environments.
Collaborate with data scientists, analysts, and business stakeholders to understand requirements and deliver scalable, reliable data solutions
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.
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Implement data quality checks, monitoring, observability, and secure coding practices aligned with governance and compliance standards
Contribute to automation, troubleshooting, and continuous improvement initiatives for data infrastructure and platforms.
Support incident response and troubleshooting related to data infrastructure and pipelines.
Engage in knowledge sharing and collaboration to foster a culture of innovation and engineer happiness.