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

Capital One•McLean, VA

5d•$215,200 - $245,600

About The Position

Data Engineer 4 (Python, AWS, Spark, Kafka, 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—focusing on data modeling, data quality, governance, permissible use, and scalable pipeline development. In this role within the Marketing and Messaging engineering group, you will be at the forefront of driving a major technical transformation. Your team builds scalable platforms that deliver hyper-personalized, omnichannel messages and experiences across owned and paid Adtech channels to help millions of Americans achieve financial empowerment.

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

Master’s Degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
7+ years of experience in application development using languages such as Python, SQL, Spark, Scala, or Java.
4+ years of hands-on experience designing, deploying, and operating data workloads in a public cloud environment (AWS, Azure, or GCP).
4+ years of experience building distributed data/computing workloads using tools like EMR, Spark, AWS Glue, Databricks, MapReduce, Hadoop, or Hive.
4+ years of experience designing, implementing, and operating real-time and streaming data applications (e.g., Kafka).
4+ years of experience designing and managing data warehousing platforms (Snowflake or Redshift) and data modeling for data warehousing.
4+ years of experience with NoSQL implementations and semi-structured data (MongoDB, Cassandra, or DynamoDB).
4+ years of experience with UNIX/Linux environments, basic commands, and shell scripting.
2+ years of experience with data orchestration (e.g., Airflow, Dagster) or data observability tools (e.g., Monte Carlo, Splunk).
2+ years of experience developing user-centric reusable data products.
2+ years of experience in Agile engineering practices and delivery environments.
Hands-on experience leveraging interactive AI tooling (e.g., Claude Code, GitHub Copilot) to accelerate software delivery.

Responsibilities

Independently design, develop, test, and deploy resilient, scalable, cloud-first data solutions, lakehouse architectures, and real-time streaming data pipelines.
Lead and influence cross-functional Agile teams (developers, data analysts, and data scientists). Enforce common data engineering design patterns to ensure code quality, maintainability, and operational efficiency.
Leverage programming languages like Python, SQL, Scala, Spark, along with NoSQL databases, open-source RDBMS, and cloud data platforms such as Databricks and Snowflake.
Implement data security standards (encryption at rest/in transit, fine-grained access control) and uphold strict standards for data quality, governance, and permissible use.
Partner with digital product managers and software engineers to deliver robust solutions that meet business demands and enhance customer experiences.
Communicate technical concepts and data outcomes clearly to both technical and non-technical stakeholders. Stay on top of tech trends, participate in engineering communities, and leverage interactive AI tooling (e.g., Claude Code, GitHub Copilot) to accelerate delivery.
Perform unit tests, conduct peer code reviews, and optimize systems for performance, observability, and downstream consumers.