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.

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.

Lead Analytics Engineer

Key skills