• Design and develop analytics-ready datasets optimized for BI consumption • Build and maintain BI-focused data models (facts, dimensions, star/snowflake schemas) to support BI semantic models • Define, document, and govern business metrics and calculations • Develop transformations and analytical models using dbt • Leverage existing SQL Server and AWS data warehouse data to create trusted analytical data sources • Partner with BI and business teams to ensure accurate, consistent, and well-defined data structures • Analyze and reverse-engineer existing datasets or reports to diagnose data or metric inconsistencies • Ensure data quality, performance, and scalability of analytical models

• Develop and maintain scalable data pipelines and ETL processes to transform raw data into usable formats. • Design, implement, and optimize data models to support reporting and analytical needs. • Collaborate with business stakeholders to understand business requirements and translate them into technical specifications. • Ensure data quality and consistency across various sources by implementing data validation and cleansing techniques. • Work with cloud-based data warehouses and analytics platforms to manage and store large datasets. • Monitor and troubleshoot data pipelines to ensure reliable and timely delivery of data. • Document data processes, workflows, and best practices to enhance team knowledge and efficiency. • Create dashboards as data products as internal or external products

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Enterprise Performance Analytics Engineer will support the development and maintenance of our analytics infrastructure, working closely with the data analysts, data engineers, and business stakeholders. This role is responsible for building and maintaining foundational data models, ensuring data quality, and enabling efficient and reliable reporting. How You'll Make An Impact - Design, build and maintain data pipelines, data marts, and other data components needed for the data analytics team. - Transform data into well-documented, tested, and trusted datasets for analysis and reporting. - Collaborate with data analysts and stakeholders to understand data needs and implement models that support dashboards, KPIs, and deep-dive analyses. - Help monitor data pipeline performance and resolve data quality issues proactively. - Contribute to documentation of data models, definitions, and data lineage. - Stay current with modern data stack tools and techniques and apply learning to support data team initiatives. - Provide ad hoc and regular cadence analytics and reporting that supports current goals and business initiatives. - Provide business support to the operations team as needed and interface with strategic customers when required. Qualifications - Proven analytical and organizational skills to meet deadlines in an environment of often-changing priorities, conceptual thinker comfortable with ambiguity. - Proficiency in SQL and data warehousing concepts. - Understanding of data modeling concepts. - Exposure to modern data stack tools. - Understanding of version control systems. - Understanding of ETL/ELT processes and data pipeline concepts. - Ability to work effectively with data analysts, engineers, and business stakeholders to understand requirements and deliver usable data assets. Requirements - Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, Statistics, Engineering, Information Systems) or equivalent practical experience. - 1-3 years of experience in a data-related role (e.g., data analyst, data engineering intern, analytics engineer). Benefits - Paid Time Off (PTO) and Company Paid Holidays. - 100% Employer paid medical, dental, and vision insurance plan options. - Health Savings Account and Flexible Spending Accounts. - Bi-weekly HSA employer contribution. - Company paid Short-Term Disability and Long-Term Disability. - 401(k) Retirement Plan, with Company Match.

• Collaborate with team members to collect business requirements, define successful analytics outcomes and design data models while considering non-functional requirements such as performance and cost constraints and security policies. • Configure ELT tools, integration platforms and orchestration systems to best practice, supporting the development of a modern cloud data warehouse that is accurate, reliable, high‑performing, and easily accessible to business teams. • Supplement the data assets created with supporting documentation to allow these assets to be easily reused by other team members and teams as necessary. • Coordinate with infrastructure and data governance teams to align with and contribute to the company data strategy. • Prioritize quality and data accuracy across all operations and deliverables.