Lead Software Engineer - Data Engineer

JPMorgan Chase

Confirmed live yesterday Low trust

Quick summary

Work type: On-site
Location: Houston, TX
Posted: 35 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $202k

$187k most similar roles pay here $230k

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.

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At a glance

TL;DR · Lead Software Engineer - Data Engineer

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Lead Software Engineer- Data Engineer, PySpark, Databricks joins the Corporate Technology Sector as part of an agile data engineering team. This role involves developing high-quality production code for data-intensive applications, creating reusable software frameworks, and mentoring other engineers. The position focuses on building a secure, scalable Global Know Your Customer and Risk Assessment Data Platform within a regulated financial services environment. Key responsibilities include designing agentic Artificial Intelligence systems, managing RAG pipelines, and governing AI-assisted engineering practices to improve code quality and delivery speed. Technical requirements include expertise in Python or Java, Spark/PySpark, Databricks, and Kafka. The role requires experience with microservices, API design, Redis, Memcached, and orchestration tools like Airflow. Candidates must also possess skills in cloud-native environments, vector stores, data lake architectures, and various observability tools like Splunk and Grafana.

Skills

Python Java LLM RAG AI ML Kafka Redis Memcached Airflow Temporal Spark PySpark Databricks Snowflake Apache Iceberg AWS Azure GCP Dynatrace Splunk Grafana Microservices API Design

What you'll do

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 human-in-the-loop controls 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 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.
Advise cross-functional teams on technical matters related to data engineering, cloud architecture, and AI/ML systems.

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.
Experience with large-scale data processing, microservices, API design, Kafka, Redis, Memcached, observability tools, and orchestration frameworks.
Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance.
Practical cloud-native experience (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, or managed endpoints (preferred).
Familiarity with AI evaluation and observability practices like red-teaming and prompt drift detection (preferred).

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