Analytics Engineer (Remote)

About the role

This remote Analytics Engineer role reports to the Director of Enterprise Data & Analytics and works closely with business teams, analysts and data engineers. You will build and maintain trusted data models and reusable data products that support reporting and automations, and help improve how data products are reviewed, released and maintained as the company grows. Working hours are US Eastern time, Monday to Friday, 9am-5pm.

Key responsibilities

• Build and maintain dbt models and certified data marts for key business domains.
• Turn business requirements into clear data definitions, models and measures with partners and analysts.
• Establish consistent business logic for shared metrics and dimensions.
• Build and maintain data quality tests, investigate failures and trace issues to their source.
• Document model purpose, ownership, definitions, dependencies and known limitations.
• Assess downstream effects before changing models, metrics or source assumptions.
• Use Git and established review and deployment practices to release changes safely.
• Support the datasets and semantic models used in the reporting layer and applications.
• Communicate tradeoffs, dependencies and delivery risks to technical and business partners.
• Complete security awareness training, escalate security risks to leadership and help remediate them.

Required profile

• Strong SQL skills and hands-on experience building production data models.
• Experience designing dimensional models and data marts, and testing, documenting and maintaining data models.
• Ability to investigate data discrepancies and explain their causes.
• Ability to work with business partners to clarify requirements and resolve differences in metric definitions.
• Strong communication, documentation and follow-through.

Required skills

• dbt or a comparable transformation framework.
• A cloud data warehouse such as Snowflake, BigQuery or Redshift.
• Familiarity with Git and collaborative code review and release practices.
• Preferred: Python for data tasks, M&A data integration, owning a semantic model, master data management, and IT, VAR, distribution or supply chain data experience.