Data Engineer 4 (Python, AWS, Kafka, Spark, SQL, Snowflake, Databricks, GenAI)
About the role
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 Data Engineers who are passionate about marrying data with emerging technologies. In this role, you’ll be at the forefront of driving a major transformation across Capital One. The Marketing and Messaging team delivers hyper-personalized messages and experiences that delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages across owned and paid Adtech channels.
Responsibilities
- 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