As a Software Development Engineer in ML, you will:
- Transition Data bricks pipelines to Workday Data Engineering tooling on AWS
- Transition Databricks notebooks to AWS SageMaker Studio
- Develop features for Workday Data Engineering tooling that promote data mesh architecture principles
- Lead teams through best practices in ML and ML Ops
- Influence the direction of our product vision and strategy with technical expertise and context
- Provide critical feedback for the team’s technical designs, architecture, and decisions
Basic Qualifications:
- 1 or more years of experience using Databricks
- 1 or more years of experience using ML pipeline tools: Airflow, Dagster, ML Flow, AWS Glue, Spark, etc.
- 2 or more years of experience using cloud compute technologies: AWS, GCP, etc.
- 3 or more years of experience building production grade software and practicing Agile methodologies
Other Qualifications:
- 2 or more years of experience building data mesh architectures
- 2 or more years of experience with ML ops
- 2 or more years of experience with machine learning and data science technologies.