Data Engineer

Location : Plano, TX (ONLY), Capital One Formers only

• On-site/Hybrid/Remote : Hybrid

The hiring team is particularly interested in candidates with the following experience:

• Strong experience with Big Data technologies and large-scale data environments.
• Proficiency in Python is preferred, along with experience in Java. Candidates who are highly skilled in one primary programming language should also have working knowledge of the other.
• Strong SQL skills and experience working with complex data environments.
• Hands-on experience with AWS and cloud technologies, including cloud-based data solutions and services.
• Experience designing, developing, and supporting data pipelines and data orchestration workflows.
• A strong Data Engineering background, with experience building and supporting scalable data solutions.
• Experience working in an Agile engineering environment and collaborating effectively with cross-functional teams.
• Hands-on experience with Kafka and event-streaming technologies.
• Experience with Spark for large-scale batch processing and distributed data workloads.
• Given the pace and immediate needs of the migration project, candidates with prior Capital One experience will be better positioned to hit the ground running with minimal ramp-up time and quickly understand the organization's environment, processes, and technical landscape.

Interview Process : One round within 1-Hour. Will be asking questions about previous Capone experience.

• Technical Assessments Required : Technical Questions with Coding questions.

Key Responsibilities

• Design, build, and operate large-scale batch and real-time data pipelines that move, transform, and publish data across the enterprise with high reliability and low latency.
• Collaborate with Agile engineering teams to architect and implement end-to-end data solutions, from data ingestion and workflow orchestration through event streaming and downstream data delivery.

Required Qualifications

• 4%2B years of experience building or operating data pipelines and orchestration systems.
• 4%2B years of data or application engineering experience with Java, Python, and SQL.
• 4%2B years of experience building and operating real-time or event-driven data systems.
• 4%2B years of experience working with distributed data and computing technologies such as Kafka, Spark, EMR, Hadoop, or equivalent platforms.
• 4%2B years of experience with cloud-based data warehousing platforms at scale.
• 4%2B years of experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• 3%2B years of experience with pipeline scheduling and workflow orchestration tools.
• 2%2B years of experience implementing secure secrets management and credential handling in production environments.
• 2%2B years of experience working in Agile engineering environments.
• Familiarity with data observability practices, including monitoring, alerting, SLA tracking, and data quality frameworks.
• Experience working in financial services or other regulated, compliance-driven industries.