Data Engineer

What You Will Do

• Design, build, and support scalable data pipelines using AWS services such as AWS Glue, AWS Lambda, Amazon S3, AWS Secrets Manager, and Amazon CloudWatch
• Develop and maintain Snowflake data models and ELT workflows to support analytics and AI/ML use cases
• Apply sound data modeling practices to create reliable, analytics‑ready data structures
• Use AI‑assisted tools to accelerate development, improve code quality, automate testing, and enhance documentation
• Support AI/ML use cases by preparing feature‑ready datasets and assisting with experimentation and production workloads
• Implement automated data validation, basic anomaly detection, and performance monitoring using AI‑enabled capabilities
• Collaborate with data scientists, analysts, and business partners to translate requirements into scalable data solutions
• Contribute to automation‑first approaches across data ingestion, transformation, deployment, and monitoring
• Follow best practices for data security, governance, access control, and data quality
• Troubleshoot pipeline issues using monitoring tools and AI‑assisted diagnostics
• Document data flows, pipelines, and operational processes using AI‑assisted documentation tools

Who You Are (Basic Qualifications)

• 3–5 years of experience in data engineering or a closely related role
• Hands‑on experience with AWS services such as AWS Glue, AWS Lambda, Amazon S3, Amazon CloudWatch, and AWS Secrets Manager
• Working experience with Snowflake, including writing optimized SQL and supporting performance‑efficient ELT workflows
• Proficiency in SQL (Snowflake), Python, and PySpark
• Experience using Git for version control and managing codebases for development and deployment
• Demonstrated use of AI‑assisted tools for faster development, troubleshooting, testing, or documentation
• Exposure to supporting analytics or AI/ML‑driven platforms
• Understanding of modern data architectures and distributed data processing concepts
• Experience working in Agile and DevOps‑oriented environments
• Willingness to adopt AI‑ready best practices and automation‑driven solutions

What Will Put You Ahead

• Exposure to SAP, SAP BW, or SAP‑based source systems
• Familiarity with data lineage, metadata, or data governance concepts
• Experience with CI/CD automation for data pipelines
• Exposure to AI‑driven ETL, data quality, or pipeline monitoring tools
• Experience with enterprise scheduling tools such as Stonebranch
  • Domain exposure to supply chain or manufacturing data