SUMMARY/OBJECTIVE

The Senior Analytics Engineer is considered a leading role in shaping the analytics platform, data modeling practices, and decision-enabling systems at isolved. As a senior individual contributor, you will act as a technical and strategic thought partner across the business-working with data engineers, analysts, and product teams to turn raw data into reliable, governed, and insightful data assets.
This is a hands-on, high-impact role that requires deep expertise in data modeling, dbt, and cloud-based data stacks, along with a strong product mindset and ability to partner closely with stakeholders to drive business value.

EXPERIENCE

Typically requires a minimum of 6 years of related experience with a bachelor’s degree; 6 years and a master’s degree; or equivalent experience.

MINIMUM QUALIFICATIONS

• 7+ years of experience in analytics engineering, data engineering, or data analytics roles.
• Expert in dbt and modern data transformation pipelines (dbt Cloud or Core)
• Deep understanding of data modeling techniques (dimensional modeling, star/snowflake schema, slowly changing dimensions, data vault, etc.)
• Proficient in SQL (performance tuning, complex joins, CTEs, window functions)
• Strong working knowledge of cloud data warehouses (e.g., Databricks preferred, Snowflake, BigQuery, Snowflake, etc)
• Comfortable with Git-based workflows, CI/CD, and software engineering best practices in analytics contexts
• Experience collaborating with cross-functional teams and translating ambiguous business needs into structured data models
• Demonstrated experience owning data domains and metrics at scale
• Strong communication and storytelling skills; able to explain complex data topics to both technical and non-technical audiences
• Proficient in Python

• Lead the design and evolution of our semantic layer and analytics data models using dbt and modern warehouse platforms.
• Define and enforce best practices in data transformation, versioning, testing, and documentation.
• Collaborate closely with analysts, product managers, and data engineers to align modeling work with business use cases.
• Own the lifecycle of metrics and definitions, ensuring consistency, trust, and reusability across the organization.
• Drive the implementation of self-service data capabilities, empowering teams through curated datasets and intuitive tooling.
• Provide technical mentorship and guidance to analytics engineers and analysts across teams.
• Act as a bridge between analytics and engineering, translating requirements into scalable and performant data infrastructure.
• Contribute to governance initiatives (e.g., data cataloging, lineage, quality monitoring) to increase trust in data products.
• Optimize and tune warehouse performance for cost and scale (Databricks preferred, BigQuery, Snowflake, etc).