Distinguished Engineer -Developer Insights & AI Telemetry

Capital One•McLean, VA

1d•$244,700 - $335,100

About The Position

As a Distinguished Engineer at Capital One, you will be part of a community of technical experts working to define the future of banking in the cloud. You will operate as a hands-on technical visionary, leading the strategy, implementation, and long-term vision for our enterprise Developer Insights domain, with a dedicated focus on Data Engineering strategies, ML/AI engineering, distributed AI telemetry, and AI Insights. In this role, you will work alongside our talented team of developers, data engineers, machine learning experts, product managers, and people leaders. Our domain-driven organizational structure drives higher accountability and ownership, empowering you to bridge the gap between deep technical implementation and executive-level business value. You will champion Capital One’s native, vendor-agnostic AI impact telemetry platform, scaling it into an enterprise-grade "Mission Control" layer that captures, analyzes, and optimizes human-AI collaboration for multi-disciplinary users. If you are ready to provide thought leadership, inject high-engineering rigor into the software delivery lifecycle, and build engineering excellence across Capital One, come join us in our mission to change banking for good.

Requirements

Bachelor’s Degree.
At least 7 years of experience in software engineering, solution architecture, or enterprise architecture.
At least 5 years of experience with big data engineering, distributed computing, or data architecture (e.g., Spark, Kafka, Kinesis, ClickHouse, Snowflake, or cloud data warehouses).
At least 3 years of experience with Big Data and data streaming
At least 4 years of experience building and optimizing production-grade ML/AI lifecycles, pipelines, or frameworks.
At least 2 years of cloud computing experience (AWS, Microsoft Azure, or Google Cloud).

Nice To Haves

Master’s Degree in Computer Science, Information Systems, Data Engineering, or a related engineering discipline.
10+ years of professional experience coding in commonly used languages like Python, Go, Java, or TypeScript.
6+ years of experience designing and managing high-throughput distributed streaming architectures or edge collection telemetry platforms (e.g., OpenTelemetry, Protobuf interception hooks).
3+ years of experience in a technical leadership role directly influencing VP/SVP/Executive stakeholders on multi-million dollar technology or AI strategy initiatives.
Deep understanding of compliance, data privacy, and cybersecurity constraints within a highly regulated environment (BankTech), including experience co-authoring risk assessments alongside Cyber and Risk teams.
Proven track record of operating within a product-centric organization (Domain Category Owner/Technical Product Owner model), delivering tactical values in continuous validation cycles.
Experience utilizing advanced engineering metrics (DORA, SPACE, AI Telemetry, Feature Lead Time) to demonstrate how developer tool investments translate into measurable corporate velocity and efficiency gains.

Responsibilities

Architect the long-term enterprise roadmap for distributed AI telemetry, streaming data engineering, and automated developer insight frameworks.
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.
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.
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.
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.
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.
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.