Data Engineer 5

Capital One • McLean, NY

7d

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. The team builds and operates platforms supporting Capital One’s data pipelines, storage, and access, and is evolving its capabilities for AI and expanding its data platforms.

The Data Engineer designs, builds, and delivers cloud-first data solutions and applications, working across Agile teams with product managers and software engineers. The role involves developing scalable, resilient, and operationally efficient data pipelines and platforms; shaping architecture and platform choices; and establishing engineering patterns that promote code quality, maintainability, and reuse. The engineer also communicates technical concepts and outcomes to stakeholders, leads major data initiatives, and mentors peers and junior engineers while contributing hands-on.

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 6 years of application development experience; 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 programming experience with at least one of 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 relational and non-relational database systems.

Nice To Haves

• Master’s degree in Computer Science or a related field.
• 8 or more years of experience in data engineering.
• 5 or more years of hands-on experience designing, deploying, and operating data workloads in AWS.
• 5 or more years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
• 5 or more years of experience designing, implementing, and operating real-time or streaming data pipelines.
• 5 or more years of experience working with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 5 or more years of experience designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 4 or more years of data modeling experience.
• 3 or more years of experience working in an Agile development environment.
• 3 or more years of experience developing user-centric reusable data products.
• 3 or more years of experience working on data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.

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 working with machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
• Use programming languages and data platforms such as Python, Spark, relational and NoSQL databases, Databricks, and Snowflake.
• Stay current with data technology trends, experiment with and learn new technologies, participate in technology communities, and mentor members of the data community.
• Collaborate with product managers and software engineers to deliver cloud-first data solutions.
• Independently design, build, and deliver cloud data solutions and applications with little or no supervisory support.
• Architect and enforce common data engineering design patterns to support code quality, maintainability, and reusability across platforms and pipelines.
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders.
• Design and build scalable, resilient, operationally efficient data pipelines and platforms.
• Contribute hands-on while mentoring peers and junior engineers and elevating team capabilities.
• Lead large-scale data initiatives end to end, make critical architectural decisions, and evaluate platform choices such as Snowflake versus Databricks against technical and business requirements.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior