This listing doesn't post a salary. Most similar roles pay
$192,050–$211,200.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Lead Software Engineer-Data Engineer, Pyspark, Databricks within the Corporate Technology Sector, you will join an agile data engineering team to build and deliver a secure, stable, and scalable Global Know Your Customer and Risk Assessment Data Platform. You will develop high-quality production code for data-intensive applications, create reusable software frameworks, and design agentic Artificial Intelligence systems including multi-agent workflows and RAG pipelines. You will drive the adoption of AI-assisted engineering practices while establishing standards for LLM-based applications in regulated environments. Your daily work involves utilizing Python, Java, PySpark, Databricks, Kafka, Redis, and Airflow to manage large-scale data processing and microservices. You will also leverage cloud-native technologies like AWS or Azure, manage vector stores, and oversee infrastructure for model serving and observability tools like Splunk and Grafana.
Develop secure, high-quality production code for data-intensive applications while mentoring other engineers.
Create durable, reusable software frameworks and patterns to be utilized across multiple teams.
Design and govern agentic AI systems including multi-agent workflows and tool-use integrations for regulated environments.
Establish engineering standards for LLM-based applications, including RAG pipelines, vector stores, and model serving.
Drive the adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
Implement automated tools within the SDLC to increase the value of automation at scale.
Ensure responsible AI usage by enforcing data sensitivity controls and secure handling of inputs/outputs.
Advise cross-functional teams on technical matters regarding cloud, AI/ML, and data engineering.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale.
Experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures in regulated environments.
Expert proficiency in one or more programming languages, particularly Python and/or Java.
Advanced knowledge of software application development and technical processes in areas such as cloud, AI/ML, or data engineering.
Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools and coaching others on compliant usage patterns.
Experience with large-scale data processing, microservices, API design, Kafka, Redis, Memcached, observability tools, and orchestration frameworks.
Advanced knowledge of relational/NoSQL databases, vector stores, data lake architectures, and cloud-native platforms (AWS, Azure, or GCP).
Experience with modern data platforms like Databricks or Snowflake (preferred).
Deep hands-on experience with Spark/PySpark and other big data processing technologies (preferred).
Expertise in open-source table formats and catalog services such as Apache Iceberg (preferred).
Experience with LLM orchestration frameworks, model serving infrastructure, and AI evaluation practices (preferred).