Lead the design, optimization, and maintenance of scalable ELT pipelines using AWS tooling, Snowflake and dbt.
Own Spark’s most critical data models, ensuring they are well-documented, tested, performant, and trusted across the organization.
Drive standardization and automation across data workflows to reduce manual effort, improve reliability, and enable faster iteration.
Partner deeply with cross-functional stakeholders to understand ambiguous business problems and translate them into clear, scalable data solutions.
Set best practices for data quality, testing, and observability, continuously raising the bar for accuracy and consistency.
Lead medium
to large-scale data initiatives end-to-end, including technical design, execution, stakeholder communication, and delivery.
Mentor and support Analytics Engineers and other Data teammates, providing technical guidance, code review, and coaching.
Contribute to data team strategy, influencing tooling decisions, modeling patterns, and long-term architecture.

5+ years of experience in analytics engineering or a closely related role.
Advanced SQL expertise, with a strong track record of building and maintaining production-grade data models.
Deep experience with dbt, including testing, documentation, and modular modeling patterns.
Proven ability to own ambiguous problems and turn them into durable, well-designed data solutions.
Strong instincts for data quality, correctness, and edge-case handling, especially in messy or incomplete datasets.
Experience automating and improving data workflows using modern cloud and analytics tooling.
Comfortable giving and receiving feedback, and helping teams converge on shared standards and best practices.
Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Senior Analytics Engineer

Key skills