Staff Data Engineer

Capital One · McLean, VA · $269,100 - $307,200 · hybrid 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. Responsibilities: Build awareness, increase knowledge and drive adoption of modern technologies, sharing consumer and engineering benefits to gain buy-in Strike the right balance between lending expertise and providing an inclusive environment where others' ideas can be heard and championed; leverage expertise to grow skills in the broader Capital One team Promote a culture of engineering excellence, using opportunities to reuse and innersource solutions where possible Effectively communicate with and influence key stakeholders across the enterprise, at all levels of the organization Operate as a trusted advisor for a specific technology, platform or capability domain, helping to shape use cases and implementation in a unified manner Lead the way in creating next-generation talent for Tech, mentoring internal talent and actively recruiting external talent to bolster Capital One's Tech talent Drive the strategic direction of the data engineering practice by continuously researching and assessing emerging trends in data, AI, and engineering, and developing a roadmap to integrate relevant innovations into the overall data platform Lead strategic alignment and integration decisions across multiple data systems and business units, championing a unified data architecture that supports seamless, scalable, and governed data flow across the organization Own the creation, management, and socialization of data strategy artifacts, ensuring comprehensive documentation is available to inform and guide enterprise-level decisions and initiatives Serve as a technical "force-multiplier," independently owning the end-to-end design and coding of critical data projects while influencing architectural standards and mentoring junior engineers to scale the team's capabilities and output 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 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 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 orch