Lead Analytics Engineer

Sundays for Dogs is a founder-led, direct-to-consumer brand focused on reimagining pet food to enhance the quality time dog owners spend with their pets. They are seeking a Lead Analytics Engineer to build their analytics foundation from scratch, creating reliable transformation pipelines and optimizing data models to support business-critical reporting and analysis.

Responsibilities:

• Develop and maintain high-quality dbt models, tests, documentation, and workflows to create reliable transformation pipelines
• Build and optimize dimensional and semantic data models that power business-critical reporting and analysis
• Partner with data consumers and stakeholders to translate business questions into well-defined datasets and semantic definitions
• Design, build, and maintain the semantic layer for visualization tools such as Omni Analytics, Looker, or Sigma
• Ensure data quality and trust by implementing automated testing, monitoring, and observability best practices
• Collaborate with Engineering to scale pipelines, improve performance, and reduce latency in the analytics stack
• Own the data modeling strategy and governance standards, including conventions, documentation, and onboarding practices
• Mentor and guide other analytics engineers and data team members on best practices

Requirements:

• 6+ years of experience in analytics engineering, data engineering, or related roles
• Deep expertise with dbt, including advanced modeling, macros and testing frameworks
• Strong SQL skills and experience with cloud data warehouses (e.g., Snowflake, BigQuery, Databricks)
• Significant hands-on experience developing in modern visualization layer platforms such as Omni Analytics, Looker, or Sigma
• A solid understanding of DTC and ecommerce metrics such as CAC, LTV, cohort analysis, retention and forecasting
• Experience translating business needs into structured, reusable analytics models
• Excellent communication skills and ability to present technical concepts to business stakeholders
• Experience with data observability and monitoring tools
• Familiarity with reverse ETL tools and workflows
• Python experience for orchestration or transformation tasks