Data Engineer 4 (Manager, IC)-EDT
About The Position
Capital One’s Enterprise Data Tech (EDT) manages data technology, platforms, and experiences across the company. Its mission is to make data easy to find, use, understand, govern, and protect throughout the data lifecycle, supporting data storage and access platforms and evolving capabilities for AI and the expansion of Capital One’s data platforms. The Data Engineer will build and support cloud-first data solutions and applications, working with Agile teams and collaborating with product managers, software engineers, developers, data analysts, and data scientists. The role focuses on scalable, resilient, secure, and operationally efficient data pipelines and platforms, while applying reusable engineering patterns and communicating technical concepts and data outcomes to stakeholders. The position also involves independently delivering solutions, keeping current with data technology, and mentoring others in the data community.
Requirements
- Bachelor’s degree or higher in Computer Science or a related quantitative field, such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
- At least 4 years of application development experience; internship experience does not apply.
- At least 2 years of experience in distributed data.
- At least 2 years of SQL experience.
- 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 relational and non-relational database systems.
Nice To Haves
- 7 or more years of application development experience, with demonstrated proficiency in Python, SQL, Scala, or Java.
- 4 or more 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 or more years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
- 4 or more years of experience designing, implementing, and operating real-time or streaming data pipelines.
- 2 or more years of experience with data observability tools (such as Monte Carlo or Splunk) or data orchestration tools (such as Airflow or Dagster).
- 4 or more years of experience working with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
- 4 or more years of experience designing and supporting data warehousing solutions such as Snowflake or Redshift.
- 2 or more years of experience working in an Agile development environment.
- 2 or more years of experience developing user-centric reusable data products.
Responsibilities
- Collaborate across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
- Influence a team of developers, data analysts, and data scientists with expertise in machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
- Use programming languages such as Python and Spark, open-source relational and NoSQL databases, and cloud-based data warehousing platforms such as Databricks and Snowflake.
- Stay current with data technology trends, experiment with and learn new technologies, participate in internal and external technology communities, and mentor members of the data community.
- Collaborate with product managers and software engineers to deliver cloud-first data solutions that support customer experiences.
- 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 support code quality, maintainability, and reusability across data platforms and pipelines.
- Represent the data engineering team and communicate technical concepts and data outcomes to internal and external stakeholders.
- Design and build scalable, resilient, and operationally efficient data pipelines and platforms that perform robustly as data volumes and business demands increase.
- Implement data security standards, including encryption at rest and in transit and fine-grained access control, and ensure compliance with data privacy regulations.
Benefits
- Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
- Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
