ML-Ops Data Engineer - Onsite Interview

·         Demonstrate expert data engineering skills supporting workflow automation using tools such as Apache Airflow, Databricks, or Azure Data Factory or ML platforms such as Dataiku or DataRobot.

·         Ability to research, analyze and resolve operational issues such as run-time failures, long-running job execution and/or platform errors in a timely manner.

·         Provide Root Cause analyses (RCA) to identify and proactively prevent future issues.

·         Expert experience working with large volumes of data to include integration and cleansing.

·         Knowledge of production support concepts such incident reporting, testing, debugging, monitoring, and audits.

·         Skill in creating and maintaining technical documentation to include run book creation for routine or repeatable operations, code annotation, and other asset documentation.

·         Strong knowledge of SQL, and Python (3+ years). Snowflake SQL experience preferred.

·         Familiarity leveraging ML algorithms such as Decision Trees, Logistic

·         Regression, Random Forest and XGBoost using an ML platform and/or an IDE such as Spyder or PyCharm.

·         Working knowledge of Tableau Desktop/Server.

Relevant Skills:

·         SQL, Python, Dataiku, Tableau

·         Ability to develop effective working relationships

·         Possessing excellent interpersonal skills to include teamwork, facilitation, and negotiation

·         Ability to work independently or as part of a team to resolve issues

·         Effective written and verbal communication skills

·         Demonstrate an ability to work under pressure, manage competing priorities and meet or exceed expectations regarding issue identification, resolutions and communication