Lead Software Engineer, Data Engineer

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Houston, TX
Employment: Full-time
Posted: 2 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $181k

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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 1219 open roles on FindRole.

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At a glance

TL;DR · Lead Software Engineer, Data Engineer

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JOB TITLE: Lead Software Engineer- Data Engineer/Pyspark/Databricks As a Lead Software Engineer within the Corporate Sector, you will join an agile data engineering team to build and deliver a secure, 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 driving the adoption of AI-assisted engineering practices. Your daily work involves managing large-scale data processing, microservices, and API design using Python, Java, Spark, and PySpark. You will utilize tools including Kafka, Redis, Airflow, and observability platforms like Dynatrace and Splunk. Technical requirements include experience with Databricks, Snowflake, Apache Iceberg, and cloud-native environments like AWS, Azure, or GCP to solve complex data governance and big data processing problems.

Skills

Python Java AI ML Data Engineering Kafka Redis MemCached Airflow Temporal AWS Azure GCP Databricks Snowflake Spark PySpark Apache Iceberg Dynatrace Splunk Grafana NoSQL Vector Stores API Design Microservices LLM Orchestration AWS Bedrock Azure OpenAI

What you'll do

Develop secure, high-quality production code for data-intensive applications and platforms.
Review and mentor other engineers to ensure engineering excellence.
Create durable, reusable software frameworks and patterns for use across multiple teams.
Drive the adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Establish consistent validation standards for secure coding, peer review, and automated testing.
Apply SDLC toolchain knowledge to improve the value of automation at scale.
Coach engineers on the safe, compliant, and responsible use of AI within engineering workflows.

What we're looking for

Hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale.
Expert in one or more programming languages, particularly Python and/or Java.
Advanced knowledge of software application development and technical processes in disciplines like cloud, AI/ML, or data engineering.
Demonstrated experience leading the use of approved AI-assisted software development tools and setting validation standards for outputs.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs.
Experience in 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 with AWS, Azure, or GCP.
Ability to present and effectively communicate with senior leaders and executives.
Experience with modern data platforms such as 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 and model serving infrastructure or managed endpoints (preferred).

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