Data Engineer 5, Insights Platform

Capital One • Plano, TX

2d • $209,000 - $238,500

About The Position

Capital One is seeking a Data Engineer to help drive a major transformation by combining data with emerging technologies. The role works across Agile teams to build, test, implement, and support technical solutions, including cloud-first data, API, and MCP solutions and applications. It involves designing scalable, resilient, and operationally efficient data pipelines and platforms, and independently making architectural and platform decisions based on technical and business needs.

The engineer will work with product managers, software engineers, data analysts, data scientists, and other developers on distributed data, machine learning, microservices, lakehouse architecture, and full-stack systems. The position also establishes reusable data engineering patterns and code-quality practices, communicates technical concepts and outcomes to stakeholders, and mentors engineers. The role uses technologies including Python, Spark, relational and NoSQL databases, Databricks, and Snowflake.

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

Responsibilities

• Collaborate across Agile teams to design, develop, test, implement, and support technical solutions.
• Influence developers, data analysts, and data scientists working with 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 including Databricks and Snowflake.
• Keep current with data trends and 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, API, and MCP solutions and applications with little or no supervisory support.
• Architect and enforce common data engineering design patterns to promote 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, and operationally efficient data pipelines and platforms.
• Contribute hands-on technical work and innovation while mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives from end to end, make architectural decisions, and evaluate platform choices such as Snowflake versus Databricks.

Benefits

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

What This Job Offers

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