Staff Engineer -Developer Insights & AI Telemetry

As a Staff Data Engineer at Capital One, you will be part of a community of technical experts working to define the future of data platforms and banking in the cloud. You will work alongside our talented team of data engineers, data scientists, machine learning experts, product managers and people leaders. Our Staff Data Engineers are leading experts in their domains, helping devise practical, scalable and reusable data solutions to complex problems. You will drive innovation at multiple levels, helping optimize business outcomes while delivering strong data and technology solutions.

At Capital One, we believe diversity of thought strengthens our ability to influence, collaborate and provide the most innovative solutions across organizational boundaries. You will promote a culture of engineering excellence and strike the right balance between lending expertise and providing an inclusive environment where the ideas of others can be heard and championed. You will lead the way in creating next-generation talent for Capital One Tech, mentoring engineers and actively recruiting to keep building our community.

Staff Data Engineers are expected to lead through technical contribution. You will operate as a trusted advisor for our key data technologies, platforms and capability domains, creating clear and concise communications, code samples, blog posts and other materials to share knowledge both inside and outside the organization. You will specialize in a particular subject area, but your input and impact will be sought and expected throughout the organization.

What You’ll Do:

• Vision & Strategy: Architect the long-term enterprise roadmap for distributed AI telemetry, streaming data engineering, and automated developer insight frameworks.
• Enterprise Scaling & Telemetry: Lead the technical evolution of our internal developer telemetry platform from a high-performance Agent Hook Architecture into a stateless, low-latency utility capable of handling billions of annual high-fidelity events across global regions.
• ML/AI & Intelligence Layer: Spearhead the deployment of advanced inference capabilities, including LLM-as-a-Judge models, to autonomously score code complexity, categorize AI use cases (e.g., Core Logic vs. Testing), and calculate the TrueThroughput and return on investment (ROI) of generative AI tools.
• Data Engineering & Architecture: Own the end-to-end data strategy, shifting from traditional batch reporting to real-time event-driven architectures utilizing a scalable Medallion (Bronze/Silver/Gold) data structure feeding directly into our enterprise data lake.
• Business Metric Correlation: Partner with Bank Tech Analytics to link AI code density data and telemetry directly with core business outcomes—such as lowering Feature Lead Time (FLT) and PR cycle time—without compromising quality and risk bars.
• Risk & Privacy Governance: Design and enforce rigorous privacy-by-design principles, implementing automated PII masking, token management, and secret filtering to guarantee a completely safe space for engineering innovation.
• Culture & Technical Contribution: Lead through direct technical contribution—prototyping systems, evaluating build vs. buy paths, authoring code patterns, and deeply mentoring principal and senior engineers across the Developer Enablement tower.

Basic Qualifications:

• Bachelor’s Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 7 years of experience in data engineering
• At least 3 years of experience in data architecture
• At least 2 years of experience building applications in AWS
• At least 5 years of experience programming with at least one of the following languages: Python, Java, or Scala
• At least 5 years of experience designing and developing data pipelines
• At least 3 years of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Preferred Qualifications:

• Master’s Degree in Computer Science or a related field
• 10+ years of experience in data engineering
• 6+ years of data modeling experience
• 2+ years of experience with ontology standards for defining a domain
• 1+ year of experience deploying machine learning models
• 10+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
• 7+ years of hands-on exp…