Data Engineer 5 (Senior Manager, IC)-Risk Tech

This role sits within Capital One's Risk Tech organization, where data engineering teams build and deploy AI-powered risk management solutions at scale. You will lead end-to-end, large-scale engineering initiatives, combining full-stack data platform development with scalable pipeline design, platform delivery, and technical mentorship. The position is based in McLean, VA with onsite expectations. What you'll do Partner across Agile teams to design, build, test, implement, and support full-stack technical solutions. Shape technical outcomes by influencing a team of developers, data analysts, and data scientists with strong experience across machine learning, distributed microservices, lakehouse architecture, and full-stack systems. Use Python and Spark alongside open-source relational and NoSQL databases and cloud data warehousing platforms such as Databricks and Snowflake. Collaborate with product managers and software engineers to deliver cloud-first data solutions that support experiences for millions of Americans. Independently design, build, and deliver cloud data solutions and applications with little or no support from supervisors or managers. Architect and enforce common data engineering design patterns to improve code quality, maintainability, and reusability across platforms and pipelines. Build data pipelines and platforms with a focus on scalability, resilience, and operational efficiency, maintaining performance as data volume and business demand grow. Serve as a data engineering ambassador, explaining technical concepts and data outcomes clearly to internal and external stakeholders to drive alignment. Lead and execute large-scale, transformative data initiatives end to end, including critical architectural decisions such as evaluating Snowflake versus Databricks based on technical and business requirements. Act as a force multiplier by balancing hands-on innovation with mentoring and elevating the skills of peers and junior engineers. Key technologies Languages: Python, SQL, Java, Scala Data & platforms: Databricks, Snowflake, EMR, Glue, Airflow, Dagster Observability & tooling: Monte Carlo, Splunk Cloud environments: AWS, Microsoft Azure, Google Cloud NoSQL & databases: MongoDB, Cassandra, DynamoDB, Redshift Required qualifications Bachelor’s degree or higher in Computer Science or a related quantitative field: Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering At least 6 years of experience in application development (internship experience does not apply) At least 4 years of experience in distributed data At least 4 years of experience with SQL At least 4 years of programming with at least one of: Python, Java, or Scala At least 4 years of experience designing and developing data pipelines At least 2 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 8+ years of experience in data engineering 4+ years of data modeling experience 9+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java 5+ years of hands-on experience designing, deploying, and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, or Google Cloud) 5+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks 5+ years of experience designing, implementing, and operating real-time or streaming data pipelines 3+ years of experience in data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster) 5+ years of experience working with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB) 5+ years of experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift) 3+ years of experience working in an Agile development environment 3+ years of experience developing user-centric reusable data products Compensation and location McLean, VA (onsite): $229,900 - $262,400 per year Richmond, VA: $209,000 - $238,500 per year Benefits Performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI) Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting overall well-being #J-18808-Ljbffr DataJobs.com