Senior Analytics Engineer

We are seeking a Senior Analytics Engineer to design, build, and maintain well-modeled analytics data in Snowflake and deliver high-quality reporting and insights using Sigma Computing as the primary BI platform.

This role spans analytics engineering and reporting responsibilities. You will develop and maintain analytics-ready data models in Snowflake using dbt, including fact tables, dimension tables, and curated reporting layers, while also building dashboards, metrics, and analyses consumed by the business. While this role does not own core ingestion pipelines or platform infrastructure, it plays a critical role in shaping how data is modeled, exposed, and used for decision-making.

You will work closely with business stakeholders to transform raw and intermediate data into trusted, well-documented Snowflake models that power consistent reporting, self-service analytics, and AI-driven use cases.

A key aspect of this role is working deeply with AI-powered analytics, including Snowflake Intelligence and Ask Sigma, to accelerate data exploration, improve insight discovery, and enable more intuitive interaction with data across the organization.

This is a hands-on role for someone who enjoys writing SQL, building dbt models, creating Sigma reports, and partnering with the business to deliver analytics that are accurate, performant, and easy to use—while leveraging modern AI tools to make analytics faster and more accessible.

• Design, build, and maintain analytics-ready data models in Snowflake using dbt, including fact tables, dimension tables, and curated marts.
• Transform raw and intermediate data into well-structured, documented, and tested reporting layers optimized for BI and analytics consumption.
• Apply analytics engineering best practices, including modular SQL, incremental models, tests, exposures, and documentation in dbt.
• Partner with data engineers to define modeling boundaries, upstream contracts, and data freshness expectations.
• Optimize Snowflake models for query performance, cost efficiency, and scalability based on Sigma usage patterns.
• Validate data accuracy and metric integrity through reconciliation, testing, and close collaboration with business stakeholders.

• Build and maintain dashboards, reports, and exploratory analyses in Sigma Computing directly on Snowflake.
• Develop and manage Sigma data models, metrics, and calculations that support consistent, trusted reporting across teams.
• Translate business questions into clear, performant Sigma workbooks using SQL, tables, pivot tables, and visualizations.
• Enable and support self-service analytics by designing intuitive datasets and providing examples, documentation, and guidance.
• Partner with stakeholders to iterate on reporting requirements and ensure insights are actionable and well understood.
• Tune Sigma performance by optimizing underlying Snowflake models, queries, and dataset structures.

• 2–5 years of experience in an analytics-focused role such as Analytics Engineer, Data Analyst, BI Engineer, or Reporting Specialist.
• Strong SQL proficiency, with hands-on experience modeling and querying data in Snowflake or a similar cloud data warehouse.
• Demonstrated experience building analytics-ready data models using dbt, including fact tables, dimension tables, and curated marts.
• Hands-on experience setting up and structuring dbt projects, including environments, model organization, testing, documentation, and exposures.
• Practical experience configuring and maintaining dbt CI/CD workflows using Git Hub, such as pull request checks, tests, and deployment to production.
• Experience delivering dashboards, reports, and self-service datasets in Sigma Computing or a comparable modern BI platform.
• Experience using Git Hub for version control, including branching strategies, pull requests, and code reviews for analytics code.
• Familiarity with CI/CD concepts as applied to analytics engineering (e.g., dbt build/test on PR, controlled promotion to production).
• Ability to collaborate with data engineers and analysts in a shared analytics codebase.
• Comfort owning analytics-layer workflows without owning underlying infrastructure.
• Strong understanding of dimensional modeling, metric definitions, and analytics best practices.
• Experience working directly with business stakeholders to define requirements and validate analytical outputs.
• Attention to data quality, testing, and documentation in production analytics environments.
• Interest in or hands-on exposure to AI-powered analytics tools, including Snowflake Intelligence and Ask Sigma.
• Curiosity about using AI-assisted workflows to accelerate analysis and improve self-service reporting.