Full-stack Engineer 4 (Apache Spark and Java)( Enterprise Platforms Technology)

Capital One•McLean, VA

3d•$197,300 - $225,100

About The Position

At Capital One, you’ll be part of a group of makers, breakers, doers, and disruptors who love solving real problems and meeting real customer needs. Enterprise Platforms Technology (EPTech) comprises many of Capital One’s most critical enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company while delivering capabilities that exemplify those standards. We are seeking a forward-thinking Full-Stack Engineer 4 to spearhead the architecture, development, and scaling of core data infrastructure and full-stack enterprise systems. In this role, you will lead a portfolio of high-impact technology projects, driving major transformations by combining traditional big data architectures, microservices, and AI-native engineering workflows in a "you build it, you own it" culture.

Requirements

Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience in software engineering (Internship experience does not apply)
At least 2 years of experience with public cloud platforms (AWS, GCP, Azure), cloud-based productivity tools, or virtualization technologies

Nice To Haves

Master’s Degree in Computer Science or a related technical field.
7+ years of professional software engineering experience coding in two or more languages: Java, Scala, Python, Go, Rust, C#, or JavaScript/TypeScript.
4+ years of experience with Advanced SQL (Open Source RDBMS) and NoSQL database management systems.
4+ years of experience creating synchronous and asynchronous APIs, alongside open-source frameworks.
3+ years of hands-on experience building and troubleshooting systems in cloud environments (AWS, GCP, Azure).
3+ years of experience with CI/CD pipelines, Infrastructure as Code (IaC), automated testing frameworks, and Git-based version control (Git, GitHub, Bitbucket).
2+ years of hands-on experience in Apache Spark performance tuning (memory configuration, partitioning, DAG optimization).
2 years of Hands-on experience with container orchestration tools, including Docker and Kubernetes.
1+ year of experience leading cross-functional engineering initiatives, establishing team coding standards, and mentoring engineers.
1+ year of experience leveraging observability platforms to identify and eliminate performance bottlenecks in distributed systems.
Proficiency in leveraging AI-native development tools and interactive AI environments (Claude Code, GitHub Copilot, Cursor, OpenAI Canvas) beyond basic code completion.

Responsibilities

Design, build, and maintain robust, highly scalable data pipelines, distributed microservices, and full-stack applications using Java, Scala, Python, Go, Node.js, and TypeScript.
Engineer backend systems capable of ingesting, processing, and serving billions of customer data points with low latency, high availability, and strong data consistency to meet critical business and regulatory needs.
Partner with architects to define service boundaries, integration patterns, and reusable frameworks across cloud-based environments.
Build cost-effective, high-throughput cloud architectures leveraging AWS services (EMR, Glue, Lambda, Step Functions, S3, RDS, DynamoDB, Kinesis).
Write optimized SQL queries across Open Source RDBMS and NoSQL systems.
Tune Apache Spark jobs (memory configuration, data partitioning, DAG optimization) to minimize compute costs and maximize pipeline performance.
Drive engineering velocity by integrating AI coding assistants and autonomous tooling (Claude Code, GitHub Copilot, Cursor, OpenAI Canvas) into daily development, testing, and refactoring workflows.
Design backend architectures and APIs that interface with AI/ML models and LLM endpoints for intelligent data enrichment and automated pipeline monitoring.
Lead, mentor, and inspire cross-functional teams of backend, data, and full-stack engineers.
Set technical standards for design, development, human-in-the-loop AI coding, and code reviews.
Implement robust monitoring, alerting, and CI/CD pipelines to ensure data quality, pipeline integrity, and zero-downtime deployments.
Proactively identify system bottlenecks, execute targeted refactoring, and reduce technical debt across distributed systems.