Data Engineer

Responsibilities:

• Design, build, and maintain ETL/ELT pipelines from enterprise applications, internal services, and third-party APIs.
• Design, develop, and operationalize robust and scalable data pipelines from enterprise applications, internal services, and third-party APIs that support business needs.
• Lead in designing and building production data pipelines from data ingestion to consumption using GCP services, Python, BigQuery, DBT, SQL, Apache Airflow, Celigo, etc.
• Drive AI adoption across the team's engineering workflows; the team has a mandate for AI adoption, and you'll be expected to be a role model, champion, remove friction, and help engineers integrate AI tools into their daily development, code review, documentation, and debugging practices.
• Design [EC5] and oversee data models in a medallion architecture.
• Mandate high standards for data validation, profiling, reconciliation, and quality initiatives.
• Stay updated with industry trends and technologies to continuously improve our data engineering practices.
• This reads to me more like an IC; also, ETL/ELT reads as data integrations; it's more data engineering. I think Mark is a better person to describe data integrations.