Data Engineer

Job Description:

• Transition Databricks 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:

• 2 or more years of experience using Databricks
• 2 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, Google Cloud Platform, 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