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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer 5 (Senior Manager, IC) - **Company:** Capital One Financial Corporation - **Location:** McLean, NY, United States - **Experience:** Expert - **Salary:** $229,900.0 - $262,400.0 - **Contract:** Internship / Graduate position - **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Cloud Database, Software Quality, Computer Programming, Data Architecture, Information Engineering, Data Systems, Data Warehousing, Software Design Patterns, Programming Tools, Distributed Data Store, Amazon DynamoDB, Python (Programming Language), Machine Learning, MongoDB, NoSQL, Open Source Technology, Standard Sql, DataOps, Software Engineering, Data Streaming, Enterprise Data Management, Data Storage Technologies, Snowflake, Apache Spark, Build Management, Information Technology, Cassandra, Non-relational Database, Data Management, Splunk, Data Pipelines, Databricks, Programming Languages, Microservices - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/data-engineer-5-senior-manager-ic-mclean-ny--d0a17dea-9215-4d4f-aea0-8244ccbda9e1 ## About the Role Integrated Circuits (ICs), Integrated Circuits (ICs), Emerging Technology, Team Player, Problem Solving Skills, Data Management, Artificial Intelligence (AI), Data Storage, Technical Leadership, Programming Tools, * Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, 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 experience programming with at least one of the following languages: 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, * Master's Degree in Computer Science or a related field * 8+ years of experience in data engineering * 5+ years of hands-on experience designing, deploying and operating data workloads in AWS * 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 * 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) * 4+ years of data modeling experience * 3+ years of experience working in an Agile development environment * 3+ years of experience developing user-centric reusable data products * 3+ years of experience working on data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster) ## Description Data Engineer 5 (Senior Manager, IC) Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who solve real problems and meet real customer needs. We are seeking a Data Engineer 5 (Senior Manager, IC) who is passionate about marrying data with emerging technologies. In this role, you'll be at the forefront of driving a major transformation across Capital One. Enterprise Data Tech (EDT) is responsible for managing data technology, platforms, and experiences across Capital One. Our core mission is to make data easy to find, use, understand, govern, and protect throughout the complex end to end data lifecycle. This foundation is built on key platforms that manage Capital One's Data Pipeline, including data storage, and access. Looking ahead, our imperatives are centered on evolving our capabilities for the AI world and strategically expanding Capital One's data platforms. What You'll Do: * Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies * Influence a team of developers, data analysts and data scientists with deep experience in machine learning, distributed microservices, lakehouse architecture, and full-stack systems * Utilize programming languages such as Python and Spark, along with open-source relational and NoSQL databases, and cloud-based data warehousing platforms including Databricks and Snowflake * Share your passion for staying on top of trends in data, experimenting with and learning new technologies, participating in internal and external technology communities, and mentoring other members of the data community * Collaborate with product managers and software engineers to deliver robust cloud-first data solutions that drive powerful experiences to help millions of Americans achieve financial empowerment * Independently design, build and deliver world-class cloud data solutions and applications with little or no support from supervisors or managers * Architect and enforce common data engineering design patterns to ensure code quality, maintainability, and reusability across data platforms and pipelines * Serve as an ambassador for the data engineering team, communicating technical concepts and data outcomes clearly to both internal and external stakeholders to drive alignment and shared understanding * Design and build data pipelines and platforms with a focus on scalability, resilience, and operational efficiency, ensuring robust performance under increasing data volume and business demands * Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers * Lead and execute large-scale, transformative data initiatives from end to end, independently driving critical architectural decisions and evaluating platform choices, such as Snowflake versus Databricks, based on technical and business requirements