Job Description
The Senior Data 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, product teams, and AI stakeholders 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, cloud-based data stacks, and AI-ready data foundations, along with a strong product mindset and ability to partner closely with stakeholders to drive business value.
Core Job Duties
Lead the design and evolution of our semantic layer and analytics data models using dbt and modern warehouse platforms, including structures that support AI-assisted analysis and reusable data products.
Define and enforce best practices in data transformation, versioning, testing, documentation, and automation of repetitive data quality checks.
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, intuitive tooling, and AI-assisted discovery where appropriate.
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, and standards for approved AI tool usage) to increase trust in data products.
Optimize and tune warehouse performance for cost and scale (Databricks preferred, BigQuery, Snowflake, etc), including identifying opportunities to automate recurring optimization and monitoring tasks.
Job Complexity
Having wide-ranging experience, uses professional concepts and company objectives to resolve complex data platform issues creatively and effectively. They are the go-to expert in one specific area of their role. Some barriers to entry exist at this level (e.g., dept/peer review)-the level at which a career may plateau.
Interaction
Works on complex issues where analysis of situations or data requires an in-depth evaluation of variable factors. Exercises judgment in selecting methods, techniques, and evaluation criteria for obtaining results. Networks with key contacts outside their own area of expertise.
Supervision
Determines methods and procedures on new assignments and may coordinate activities of other personnel.
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 combination of education and hands-on experience in data platform engineering, infrastructure engineering, or data engineering.
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…