This listing doesn't post a salary. Most similar roles pay
$192,050–$223,750.
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 Senior 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 mentor other engineers while defining architecture and engineering standards. Your work involves designing agentic Artificial Intelligence systems, RAG pipelines, and vector store integrations within regulated financial services environments. You will utilize Python, Java, Spark, PySpark, Databricks, Kafka, Redis, and Airflow to manage large-scale data processing. Additionally, you will drive the adoption of AI-assisted engineering practices and manage infrastructure across cloud platforms like AWS, Azure, or GCP while ensuring robust data governance and observability using tools such as 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 used across multiple teams and functions.
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 automation capabilities within the software development life cycle toolchain to increase value at scale.
Ensure responsible AI usage by enforcing data sensitivity controls and secure handling of inputs and outputs.
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 knowledge in one or more programming languages, particularly Python and/or Java.
Advanced knowledge of software application development and technical processes in disciplines 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 in 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).