Staff Data Engineer - (Director, IC - Card Tech)

Capital One is hiring a Staff Data Engineer (Director, IC - Card Tech) to help shape the future of data platforms and banking in the cloud. This role focuses on scalable, reusable data solutions, strategic architecture leadership, and hands-on delivery while mentoring and influencing engineering talent across the organization.

Location and Work Model

• McLean, VA (hybrid)

This role is designated as hybrid and requires adherence to Capital One’s hybrid policy for the required on-site presence at your designated office location.

Compensation

• McLean, VA: USD 269,100 - 307,200 per year
• Richmond, VA: USD 244,700 - 279,200 per year

Performance based incentive compensation may include cash bonus(es) and/or long term incentives (LTI).

Responsibilities

• Build awareness and drive adoption of modern technologies by communicating consumer and engineering benefits to gain buy-in
• Balance lending expertise with an inclusive environment where others’ ideas can be heard and championed; apply expertise to grow skills across the broader Capital One team
• Promote engineering excellence by pursuing opportunities to reuse and innersource solutions
• Communicate with and influence key stakeholders across the enterprise at all organizational levels
• Act as a trusted advisor for a specific technology, platform, or capability domain to shape use cases and implementation in a unified way
• Develop next-generation Tech talent by mentoring internal talent and actively recruiting external talent
• Set the strategic direction for data engineering by continuously researching and assessing emerging trends in data, AI, and engineering, then building a roadmap to integrate relevant innovations into the overall data platform
• Lead strategic alignment and integration across multiple data systems and business units, championing unified data architecture for seamless, scalable, and governed data flow
• Own the creation, management, and socialization of data strategy artifacts, including comprehensive documentation for enterprise decisions and initiatives such as data governance policies, architecture blueprints, and data product roadmaps
• Serve as a technical force-multiplier by independently owning end-to-end design and coding for critical data projects, influencing architectural standards and mentoring junior engineers

Core Requirements

• 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 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 end-to-end data solutions using both relational and non-relational database systems

Technologies

• Python
• Java
• Scala
• AWS
• Relational databases
• Non-relational database systems

Benefits

• Comprehensive, competitive, and inclusive set of health, financial, and other benefits that support total well-being
• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

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 experience designing, deploying and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, or Google Cloud)
• 6+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
• 6+ years of experience designing, implementing, and operating real-time or streaming data pipelines
• 4+ years of experience working on data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster)
• 6+ years of experience with unstructured/semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
• 6+ years of experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
• 4+ years of experience working in an Agile development environment
• 4+ years of experience developing user-centric reusable data products