Senior Director, Data Engineer

Capital One • McLean, VA

11d • $286,200 - $392,000

About The Position

As a Senior Director, Data Engineer at Capital One, you will lead high-performing engineering teams working to define the future of data platforms and banking in the cloud. You will partner closely with product managers, data scientists, architects and executive leaders to deliver scalable, data-driven solutions. Our Senior Directors are technical people leaders who drive data engineering strategy, guide complex architectural decisions and translate business goals into impactful data platforms.

Senior Directors are expected to lead through technical vision, operational rigor and strategic alignment. You will serve as a key owner for your domain's technical health, delivery velocity and system resilience. While shaping long-term data strategy and platform roadmaps, you will inspire a culture of continuous learning, innovation and high-impact delivery across your organization.

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences — and to protect those customers with world-class security. Our investments in technology infrastructure and world-class talent position us to be at the forefront of enterprises leveraging AI, including in the defense of the enterprise itself.

The Identity Analytics & Intelligence organization sits at that intersection. We build the data foundations, ML systems, and security products that decide - in real time - who and what can access Capital One's systems, and that detect and respond when something looks wrong. As identity moves from a human problem to a machine and agentic one, this organization is defining how a Fortune 100 bank secures workloads, non-human identities, and AI agents at scale.

We are looking for a senior technology executive to lead this organization: someone who sets multi-year platform strategy and owns the architecture, who can partner with the CISO and C-suite in the morning and pressure-test a model design or a streaming pipeline in the afternoon.

Requirements

• Bachelor’s Degree in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 9 years of experience in data engineering
• At least 7 years of people management experience
• At least 7 years of experience programming with at least one of the following languages: Python, Java, or Scala
• At least 5 years of experience driving technical delivery of roadmap features
• At least 6 years of experience designing and developing data pipelines
• At least 4 years of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Nice To Haves

• Master’s Degree in Computer Science or a related field
• 12+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
• 8+ years of hands-on experience designing, deploying and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, or Google Cloud)
• 8+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
• 8+ years of experience designing, implementing, and operating real-time or streaming data pipelines
• 6+ years of experience working on data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster)
• 8+ years of experience working with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
• 8+ years of experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
• 6+ years of experience working in an Agile development environment
• 6+ years of experience developing user-centric reusable data products
• 5+ years of experience in Data Governance, Data Governance Platforms, Data Standardization, and Data Modeling

Responsibilities

• Own the enterprise strategy and multi-year roadmap for identity analytics, access decisioning, and AI/ML security platforms — aligning AI/ML investment with business, audit, and regulatory priorities, and partnering with C-suite and CISO-org stakeholders to fund next-generation capabilities.
• Lead and grow a 50+ person organization across Data Engineering, ML, Generative AI, and Security Products, including managers and senior individual contributors. Hire, develop, and retain top technical talent; set engineering standards and a high bar for well-managed delivery.
• Set technical direction hands-on. Review architectures, model designs, and system trade-offs. Stay close enough to the code and the data to make good calls under uncertainty and to earn credibility with the engineers you lead.
• Architect low-latency, event-driven systems for real-time identity decisioning and threat detection based on streaming telemetry, behavioral signals, and contextual graph data.
• Design, build, and maintain robust ML infrastructure and pipelines supporting end-to-end workflows: feature extraction, training, testing, guardrails, evaluation, deployment, and both real-time and batch inference — with high performance, scalability, and reliability.
• Drive the evolution of MLOps practices through automated, metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring and observability for models in production.
• Build the intelligence layer for agentic and non-human identity — combining behavioral analytics, ML-based intent determination, and context graphs to secure AI agents and workload identities.
• Apply state-of-the-art LLM and GenAI techniques in production for threat-intelligence summarization, automated incident triage, and adaptive access-policy recommendation — and introduce optimization techniques that improve scalability, cost, latency, and throughput of large-scale production AI systems.
• Operate an automated governance function with pipelines from enterprise and IGA platforms, operationalizing NIST controls with continuous control validation and audit-ready evidence.
• Lead enterprise readiness for emerging cyber risk posed by frontier AI models and agentic attack patterns.

Benefits

• performance based incentive compensation
• cash bonus(es)
• long term incentives (LTI)
• comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being

What This Job Offers

• Job Type: Full-time
• Career Level: Senior