Lead Analytics Engineer
Job Summary
As a Lead Analytics Engineer, you will architect the data foundation that powers strategic organizational decisions. You will transform raw data into trusted, reusable data products using DBT and modern data tools to enable self-service analytics. This role bridges the gap between expert data engineering and high-impact business strategy.
Responsibilities
- Architect and maintain data foundation and semantic layers
- Design and build modular DBT transformations
- Partner with stakeholders to define business metrics
- Implement data quality, CI/CD, and monitoring
- Lead and mentor junior engineering team members
Required Skills
- 6+ years experience with modern data stack
- Expert SQL and query optimization skills
- Advanced DBT proficiency
- Dimensional modeling and data warehouse design
- Python or Ruby for data automation
Job Details
Responsibilities:
Build Data Products
- Design and build dimensional models, data marts, and semantic layers using DBT that serve as the single source of truth.
- Create modular, reusable data transformations that embody business logic and can be leveraged across multiple use cases.
- Develop comprehensive documentation that makes your models accessible to all users.
Partner with the Business
- Collaborate with stakeholders to deeply understand business domains, metrics, and analytical needs.
- Translate complex requirements into scalable data architectures and logical models.
- Design data structures that support both reporting and operational analytics.
Drive Technical Excellence
- Write clean, efficient, performant SQL for large-scale data processing in Snowflake.
- Implement data quality checks, testing frameworks, and monitoring to ensure reliability.
- Leverage Git, CI/CD, and software engineering best practices in analytics workflows.
Lead & Mentor
- Establish and evangelize best practices in analytics engineering and DBT development.
- Mentor junior engineers through code reviews and technical guidance.
- Contribute to the evolution of our data architecture and development standards.
- Collaborate with US-based teams to gather and understand business requirements.
Requirements:
- 6+ years of hands-on experience with the modern data stack (DBT, Snowflake, SQL) and expert-level SQL skills, including query optimization.
- Strong proficiency in DBT - macros, packages, incremental models, testing, and documentation.
- Deep understanding of dimensional modeling, star schemas, and data warehouse design patterns.
- Experience using Python and/or Ruby for data transformation and automation.
- Proven ability to collaborate with business stakeholders, translate requirements into technical solutions, and deliver reusable data products for self-service analytics.
- Strong communicator with experience leading projects and mentoring team members across technical and non-technical domains.