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Tech Stack
Tools & technologiesAWSAzureCloudETLGoogle Cloud PlatformJavaNoSQLPythonScalaSQL
About the role
Key responsibilities & impact- Design, develop, and maintain scalable data pipelines and ETL/ELT processes supporting AI research, prototyping, and production use cases.
- Collaborate with AI scientists and engineers to translate data requirements into ingestion, transformation, and serving solutions.
- Apply data governance and security controls ensuring compliance, auditability, and protection of sensitive information.
- Monitor, troubleshoot, and resolve data pipeline failures, performance issues, and schema changes.
- Continuously improve reliability through testing, observability, documentation, and automation.
- Design and maintain efficient data models supporting analytics, ML workflows, and AI agent operations.
Requirements
What you’ll need- Typically 3+ years of professional experience in data engineering, including production-grade pipeline development.
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
- Strong proficiency in Python; working experience with Java or Scala.
- Solid knowledge of SQL and experience with NoSQL databases.
- Familiarity with data warehousing and lakehouse platforms.
- Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
- Experience with orchestration frameworks and CI/CD practices for data pipelines.
Benefits
Comp & perks- No information provided on benefits or perks.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data engineeringETLELTPythonJavaScalaSQLNoSQLdata warehousingcloud platforms
Soft Skills
collaborationtroubleshootingproblem-solvingdocumentationautomation
Certifications
Bachelor’s degreeMaster’s degree
