Lead Software Engineer - Big Data

JPMorganChase•Plano, TX

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corporate Sector - Infrastructure Platforms - Data and Speciaity Services team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. This lead engineer will be focused on designing, automating, and operating scalable ETL/data transformation pipelines to production. They will work with stakeholders to gather requirements, build and optimize Spark-based batch/real-time workflows and data lake tables (Iceberg/Delta), contribute to platform/SDK infrastructure, improve cost and reliability through monitoring (Grafana/Prometheus) and CI/CD, and mentor junior engineers. Heavy experience with Spark (Scala/Python/Java), distributed data stores (HBase/Cassandra), cloud data tooling (Azure/AWS), and API delivery via Spring Boot/Docker with Git-based collaboration would be ideal.

Requirements

Formal training or certification on Software engineering concepts and 5+ years applied experience
Advanced in one or more programming language(s) and framework(s), i.e., Python, Java, Scala, Apache Spark, Databricks, Grafana, Prometheus, Elasticsearch, Cloudwatch, Spring Boot API, and containers
Implementing low-latency, scalable data operations and supporting real-time lookups, updates, and analytics using Apache HBase and Apache Cassandra
Build, design and implement scalable ETL pipelines to process structured and semi-structured data and implement partitioning within Hadoop-based architectures
Managing large-scale data lake tables in Iceberg format and proficiency in automation and continuous delivery methods also supporting real-time and batch data ingestion, data cleansing, and transformation, and feature extraction on Spark
Implementing ACID-compliant data operations and enabling schema evolution using Delta table structures
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Configuring and maintaining Grafana dashboards integrated with Prometheus, Elasticsearch, or CloudWatch to monitor pipeline performance, API services, and system health in real time
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, also with documenting data workflows, Spring Boot API specifications, CI/CD processes, Grafana configurations, and cloud architecture using Confluence
Demonstrated proficiency in software applications and technical processes within a technical discipline and proficient in all aspects of the Software Development Life Cycle (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Nice To Haves

In-depth knowledge of the financial services industry and their IT systems
Practical cloud native experience
Creating and deploying RESTful APIs using Spring Boot in Docker containers to deliver processed data access and operational insights
Managing source code to maintain structured development workflows, version control, and team collaboration using Git with GitHub and Bitbucket
Building, deploying, and managing scalable data engineering pipelines and analytics infrastructure using Azure Data Factory, Databricks, or AWS tools such as EC2, S3, EMR, Lambda, Glue, IAM, or CloudWatch

Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Review, understand, code, optimize, and automate existing one-off data transformation pipelines into discrete, scalable tasks
Plan, design, and implement data transformation pipelines and monitor operations of the data platform in a production environment
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems and also plan, design, and implement data transformation pipeline to monitor the operations of data platforms in a production environment
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review / refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation, collaborating with internal clients and service delivery engineers to identify data needs and intended workflows, and troubleshoot to find workable solutions
Gather, analyze, and document detailed technical requirements to design and implement solutions, and disseminate information to guide other engineers
Contribute code to the underlying infrastructure, software development kits, and platforms being built to support bespoke data transformation pipelines and enable predictive models to be produced and run at scale
Identify engineering opportunities to optimize operational effort and running costs of the data platform
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies, and mentor junior engineering staff - providing guidance on day-to-day code development work
Adds to team culture of diversity, opportunity, inclusion, and respect