Job description

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 audiences • Proficient in Python, with the ability to automate data workflows and support AI-enabled analytics use cases as needed Nice to Have: • Experience with modern BI tools (PowerBI, Looker, Mode, Hex, Tableau, etc.) • Some exposure to Spark • Background in experimentation (e.g., A/B testing infrastructure and analysis) About isolved isolved is a leading provider of human capital management (HCM) solutions that combines modern technology with expert services and support. Purpose-built for People Heroes™, isolved gives HR, payroll and benefits leaders the tools and insights to streamline operations and deliver employee experiences that matter. isolved People Cloud™ is a connected HCM platform with built-in artificial intelligence (AI) and analytics that brings together HR, payroll, benefits, workforce management and talent management in one experience. Built on a legacy of 40 years in the market, isolved is trusted by more than 200,000 employers and used by 9 million U.S. employees, representing about one in 20 American workers. Visit www.isolvedhcm.com. EEO Statement isolved is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. isolved is a progressive and open-minded meritocracy. If you are smart and good at what you do, come as you are. Disability Accommodation Visit www.isolvedhcm.com/careers for more information regarding our incredible culture and focus on our employee experience. Visit www.isolvedeebenefits.com for a comprehensive list of our employee total rewards offerings. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions outlined above.

Requirements

Must-haves

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.
Job Complexity
Determines methods and procedures on new assignments and may coordinate activities of other personnel.
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)
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

Nice-to-haves

Optimize and tune warehouse performance for cost and scale (Databricks preferred, BigQuery, Snowflake, etc), including identifying opportunities to automate recurring optimization and monitoring tasks.
Strong working knowledge of cloud data warehouses (e.g., Databricks preferred, Snowflake, BigQuery, Snowflake, etc)
Experience with modern BI tools (PowerBI, Looker, Mode, Hex, Tableau, etc.)
Some exposure to Spark
Background in experimentation (e.g., A/B testing infrastructure and analysis)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigosqlci/cdpython

Platform: isolved
Effort: medium

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