Senior Data Platform Engineer - Payments & Analytics

Aumni

Plano (TX)

On-site

USD 110,000 - 150,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. is seeking a Software Engineer III to join the Sales Enablement Data Technology team. You will be part of an agile team delivering secure, scalable data solutions across cloud and on-premises environments.

You will build and maintain data platforms using Databricks, Snowflake, Iceberg, PostgreSQL, and Oracle, focusing on data quality, performance, and reliability while supporting analytical and operational workloads.

Qualifications

Formal training or certification on software engineering concepts and 3+ years applied experience.
Experience with Big Data ecosystems (Databricks, Snowflake, Iceberg) and relational/NoSQL databases.
Proficiency in Python and Java; familiarity with cloud platforms (AWS, Azure, GCP).
Hands-on experience using enterprise-authorized AI-assisted software development tools with evaluation for correctness, performance, and security.
Understanding of responsible AI use, data sensitivity, secure handling, and resiliency.
Experience in developing, debugging, and maintaining code in a large corporate environment with modern languages and SQL.
Knowledge of SDLC and agile methodologies (CI/CD, security, resiliency).
Knowledge of data modeling, ETL/ELT, data warehousing, security/compliance.
Awareness of security best practices and compliance in data handling.
Strong problem-solving and ability to work independently and collaboratively.

Responsibilities

Develop, optimize, and maintain data pipelines and ETL for large-scale ingestion and integration across cloud and hybrid environments.
Collaborate with data scientists, analysts, and stakeholders to deliver scalable data solutions.
Leverages enterprise-authorized AI coding assist tools to improve code quality, delivery speed, and productivity, while validating outputs.
Applies knowledge of SDLC toolchain to improve automation value.
Produces architecture and design artifacts for complex applications and ensures design constraints are met by code.
Gathers, analyzes, and visuals from large data sets for continuous software improvement.
Identify hidden data patterns to drive improvements in coding hygiene and architecture.
Implement data quality checks, monitoring, observability, and secure coding practices.
Contribute to automation, troubleshooting, and continuous improvement of data infrastructure.
Support incident response and troubleshooting related to data pipelines.
Engage in knowledge sharing to foster innovation and engineer happiness.