Role Description - Design, build, and maintain scalable data pipelines to process and analyze large datasets - Integrate data from various sources, ensuring data quality and consistency - Develop and manage ETL processes to transform raw data into usable formats - Create and maintain data models to support reporting and analytics - Work closely with operations analysts and stakeholders to understand data requirements and deliver solutions - Optimize data processing workflows for performance and scalability - Implement data validation and quality assurance processes to ensure accuracy and reliability - Document data processes, workflows, and systems for transparency and knowledge sharing - Troubleshoot and resolve data-related issues and provide support for data-related inquiries - Stay updated with the latest industry trends and best practices in data engineering and analytics - Ensure data security and compliance with relevant regulations and standards - Manage and prioritize multiple projects and tasks effectively to meet deadlines - Become Workato Automation Pro I, II, and III certified to create recipes facilitating data ingestion and auditing - Full-time telecommuting is an option Qualifications - Bachelor's degree (or foreign equivalent) in Computer Science, Business Analytics, Data Analytics, Information Systems, Statistics, Engineering, or related quantitative discipline - 2 years of experience in the job offered or a related occupation Requirements - SQL - dbt - Snowflake - Salesforce - Predictive Analysis - Data Governance - Relational Databases - CI/CD - Data Modeling - SchemaChange - Python - NetSuite - Must be legally authorized to work in the U.S. without sponsorship Benefits - Vibrant and dynamic work environment - Multitude of benefits for employees to enjoy inside and outside of their work lives

• Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and ad hoc analysis. • Collaborate with data engineering teams to expand and optimize the analytics warehouse architecture. • Establish and enforce standards for schema design, naming conventions, and data lineage. • Develop, maintain, and optimize data pipelines that deliver clean and reliable data to business users. • Partner with stakeholders to gather requirements and translate business needs into scalable data solutions. • Create and maintain comprehensive data documentation, including metrics definitions and transformation logic. • Support BI platforms by ensuring analytics-ready datasets and assisting with dashboard development. • Monitor data quality and implement testing, alerting, and validation processes to ensure warehouse reliability. • Identify opportunities to improve data accessibility, consistency, and usability across the organization. • Promote best practices in analytics engineering, including version control, code reviews, and reproducible workflows.

• Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and ad hoc analysis. • Collaborate with data engineering teams to expand and optimize the analytics warehouse architecture. • Establish and enforce standards for schema design, naming conventions, and data lineage. • Develop, maintain, and optimize data pipelines that deliver clean and reliable data to business users. • Partner with stakeholders to gather requirements and translate business needs into scalable data solutions. • Create and maintain comprehensive data documentation, including metrics definitions and transformation logic. • Support BI platforms by ensuring analytics-ready datasets and assisting with dashboard development. • Monitor data quality and implement testing, alerting, and validation processes to ensure warehouse reliability. • Identify opportunities to improve data accessibility, consistency, and usability across the organization. • Promote best practices in analytics engineering, including version control, code reviews, and reproducible workflows.

• Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and ad hoc analysis. • Collaborate with data engineering teams to expand and optimize the analytics warehouse architecture. • Establish and enforce standards for schema design, naming conventions, and data lineage. • Develop, maintain, and optimize data pipelines that deliver clean and reliable data to business users. • Partner with stakeholders to gather requirements and translate business needs into scalable data solutions. • Create and maintain comprehensive data documentation, including metrics definitions and transformation logic. • Support BI platforms by ensuring analytics-ready datasets and assisting with dashboard development. • Monitor data quality and implement testing, alerting, and validation processes to ensure warehouse reliability. • Identify opportunities to improve data accessibility, consistency, and usability across the organization. • Promote best practices in analytics engineering, including version control, code reviews, and reproducible workflows.