Data Engineer 4 (Python, AWS, Kafka, Spark, SQL, Snowflake, Databricks, GenAI)

Capital One•McLean, VA

1d•$179,400 - $245,600

About The Position

Data Engineer 4 (Python, AWS, Kafka, Spark, SQL, Snowflake, Databricks, GenAI) 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.

Requirements

Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience in application development (Internship experience does not apply)
At least 2 years of experience in distributed data
At least 2 years of experience with SQL
At least 2 years of experience with one programming language (Python, Java, or Scala)
At least 2 years of experience in data pipeline design and development
At least 1 year of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Nice To Haves

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

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 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
Implement data security standards, including encryption at rest/transit and fine-grained access control, ensuring compliance with data privacy regulations