Data Engineer

Join a rapidly growing AI and data engineering team as a Data Engineer, developing and supporting cloud-native data ingestion solutions in a hybrid Richmond, VA environment. You will architect, build, and optimize critical data pipelines that enable secure and compliant movement of financial data between enterprise systems and third-party applications. This role offers long-term career growth, hands-on experience with advanced ML Ops and ML engineering tools, and daily collaboration with experts in AWS-native technologies., Design, build, and maintain scalable data pipelines using Python and AWS-native services.

• Develop robust workflows for data ingestion, validation, tokenization, transformation, and publishing.
• Implement and manage distributed data processing solutions using AWS Glue, Spark, Lambda, ECS, and Flink.
• Develop and maintain cloud infrastructure with AWS CDK and Infrastructure as Code best practices.
• Create and execute comprehensive unit, integration, and end-to-end tests to ensure platform stability.
• Monitor, troubleshoot, and resolve production issues across the entire data platform.
• Ensure data security and compliance with enterprise data protection and governance standards.
• Remediate security vulnerabilities and manage platform dependencies.
• Collaborate closely with engineering and platform teams to deliver high-performing, reliable data solutions.
• Contribute to CI/CD pipelines and modern software engineering practices.

Proven experience in Python development within enterprise-scale environments.

• Hands-on expertise with AWS services: Lambda, ECS, Kinesis, S3, DynamoDB, IAM, CDK.
• Proficiency with Spark and AWS Glue for large-scale data processing.
• Experience with Git, source control management, and CI/CD pipelines.
• Databricks experience for collaborative analytics and processing.
• Demonstrated success building and supporting cloud-native data pipelines and ingestion frameworks.
• Strong foundation in Infrastructure as Code, data validation, transformation, and data quality.
• In-depth knowledge of secure software development and vulnerability remediation.
• End-to-end testing, system testing, and production deployment support.
• Exceptional troubleshooting skills in distributed systems.

Preferred Skills

• Experience in financial services or regulated data environments.
• Expertise in sensitive data handling, tokenization, or data governance.
• Familiarity with Apache Flink, real-time streaming, and event-driven architectures.
• Knowledge of containerized deployments and orchestration platforms.
• Background in automated testing frameworks and test-driven development.
• AWS certifications (e.g., Solutions Architect Associate, Developer Associate).
• Agile team experience., Must haves:– Required SkillsPython development in enterprise-scale environmentsAWS services including: Lambda ECS Kinesis S3m DynamoDB IAM and CDKSpark and AWS Glue for large-scale data processingGit source control management and CI/CD pipelinesDatabricks–Required Knowledge & ExperienceExperience building and supporting cloud-native data pipelines and data ingestion frameworksExperience developing and maintaining Infrastructure as Code solutionsStrong understanding of d Transformation and data quality practicesKnowledge of secure software development practices and vulnerability remediationExperience supporting end-to-end testing System testing and production deploymentsStrong problem-solving and troubleshooting skills in distributed systems environments

Long-term project with significant opportunities for career growth.

• Be part of a dynamic, rapidly expanding team at the forefront of AI data engineering.
• Exposure to ML Ops and ML engineering tools in a fully AWS-native environment.
• Hybrid work flexibility based in Richmond, VA.
• No sponsorship required; all candidates must be authorized to work in the U.S.