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Remote role, popular Data Engineer title, and metro locations increase candidate density.
Data engineering skills are transferable across industries, though platform-specific tools create moderate domain bias.
Explicit 10+ years requirement and mandatory cloud, Python, and Terraform skills enforce strict filtering.
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Design and implement high-performance, real-time data pipelines to support autonomous systems and agentic AI.
Own end-to-end development including architecture, coding, unit testing, integration, CI/CD pipeline creation, and deployment.
Provide development work estimates and guide feature and technology decisions impacting product success.
Minimum 10 years of experience in data engineering or related field.
Strong proficiency in Python programming for building and maintaining data pipelines.
Hands-on experience with AWS services including Networking, IAM, Glue, S3, Lambda, Firehose, RDS, Event Bridge, and Cloudwatch.
Experience with CI/CD pipelines (e.g., Jenkins, GitLab) and infrastructure as code tools like Terraform; knowledge of infrastructure security best practices.
Experienced in building scalable data infrastructure that supports complex AI-driven applications in large enterprise environments.
Skilled at independent end-to-end delivery with clear communication, problem-solving, and collaboration to meet functional objectives.
Familiarity with advanced data platforms (e.g., Teradata, Starburst), API-based data integration, cloud cost/performance optimization, and continuous operational monitoring.