Analytics Engineer

Purple Wave is seeking a skilled and curious Analytics Engineer to join our growing data organization. Reporting to the Business Intelligence Manager, this role sits at the intersection of data engineering and analytics, ensuring that raw data is transformed into clean, tested, well-documented datasets that the entire company can trust and use. This is a remote-eligible individual contributor role responsible for building and maintaining the modeling layer that powers reporting, self-service analytics, and data-driven decision-making across the business. The Analytics Engineer owns the transformation layer between raw ingested data and the curated, business-ready datasets consumed by analysts, stakeholders, and downstream applications. Applying software engineering best practices -- version control, testing, documentation, and CI/CD -- to the analytics workflow, this role ensures that data models are reliable, performant, and easy to understand. The Analytics Engineer partners closely with data engineers, analysts, data scientists, and business stakeholders to translate business logic into modular, maintainable data models and to establish a single source of truth for key metrics and dimensions. Responsibilities: Data Modeling & Transformation Design, build, and maintain dimensional and analytical data models using dbt (data build tool). Transform raw and staged data into clean, well-structured datasets optimized for analysis and reporting. Define and codify core business logic, metrics, and definitions in a central, version-controlled modeling layer. Develop and enforce modeling conventions (naming, structure, materialization strategies) to ensure consistency across the project. Data Quality & Testing Write and maintain robust data tests (schema, freshness, custom) to catch issues before they reach consumers. Monitor data pipeline health and model freshness, triaging and resolving failures promptly. Collaborate with data engineers to improve source data reliability and establish data contracts where appropriate. Proactively identify data quality gaps and build validation layers to ensure accuracy, completeness, and consistency. Documentation & Governance Maintain thorough, up-to-date documentation for all models, sources, and metrics within the dbt project. Contribute to a governed semantic layer that provides consistent metric definitions across BI tools and stakeholders. Support data cataloging efforts so business users can discover, understand, and trust the data they consume. Partner with stakeholders to define and document data ownership, lineage, and access policies. Analytics Enablement & Collaboration Partner with analysts and business stakeholders to understand reporting needs and translate them into scalable data models. Enable self-service analytics by building intuitive, well-documented datasets that reduce ad-hoc data requests. Support the creation of dashboards and reports in BI tools (e.g., Tableau, Power BI) by ensuring a reliable and performant data layer. Collaborate with data scientists to prepare and curate feature datasets for statistical models and machine learning workflows. Design and maintain analytical dashboards as necessary. Engineering Best Practices Manage the dbt project using version control (Git), including branching strategies, pull requests, and peer code review. Build and maintain CI/CD pipelines for automated testing and deployment of data models. Optimize model performance through materialization strategies, incremental builds, and query tuning within the cloud data platform. Evaluate and recommend tools, packages, and patterns that improve team velocity and model quality. Undertake additional assigned duties as requested. Supervisory Responsibilities: None. Qualifications: High school diploma or GED required. 2+ years of experience in analytics engineering, data analytics, or data engineering roles. Proficiency with dbt (dbt Core or dbt Cloud) and strong SQL skills. Experience with modern cloud data platforms (e.g., Databricks, Snowflake, BigQuery, Redshift). Solid understanding of dimensional modeling, data warehousing concepts, and ELT patterns. Working knowledge of version control (Git) and CI/CD workflows. Familiarity with at least one BI tool (e.g., Tableau, Power BI, Looker). Strong communication skills with the ability to translate business requirements into technical data models. Experience with Python is a plus but not required. Exposure to semantic layers, metrics frameworks, or data cataloging tools is highly preferred. A collaborative mindset and comfort working across technical and non-technical teams. Spanish speaking bi-lingual candidates are encouraged to apply. Candidates may be requested to complete position specific skills assessments. Applicants must be either a U.S. Citizen or eligible to work in the U.S. Requires the ability to satisfactorily complete a background check. Working Settings: Full-time Salaried Exempt, not eligible for overtime. Office hours are 8am-5pm, Monday through Friday, Central Time zone, additional hours may be required depending on priorities. This position is remote work eligible within the United States. Please be aware: the first week of employment includes mandatory in-person training. Remote start arrangements are not available. Also mandatory: One week a year of in-person training with the department. Potential for 10% travel, should the need arise. Prolonged periods sitting at a desk and working on a computer. Must be able to lift up to 15 pounds at times. Compensation: The salary varies based on experience and qualifications, but typically ranges from $80,200 to $110,400 per year. Monthly Bonus Program - determined by the Company's monthly revenue result and are paid on a "percent to plan" payout formula (90% = $300, 100% = $600, 110% = $900, 120% = $1,200). Monthly phone stipend in accordance with the Company's cell phone policy, currently $120/month. Health insurance, Dental insurance, and Vision insurance. 401(k) plan with an employer match up to 4% starting the first day of employment. Company-paid Life Insurance with options for supplemental coverage. Fully paid Short-Term Disability provided by the Company. 3 Weeks of PTO annually (details shared during onboarding). Employee Stock Purchase Program (ESPP) - Eligible to purchase company stock at a discount after 90 days of employment, with enrollment opportunities each May and November.