Analytics Engineer

WHO WE ARE: Quartile, the world's largest retail media optimization platform, is a trusted partner for multichannel e-commerce success. Through unmatched expertise and patented AI technology, we fuel growth for 5,300+ brands and sellers worldwide and manage an annual ad spend exceeding $2 billion. The award-winning platform covers major marketplaces and ad channels for optimal reach. The result is unprecedented granularity, smarter budgeting, and bespoke solutions for retailers. Quartile is proud to be an equal opportunity employer with employees stemming from a wide range of backgrounds and experiences. As a business, we value the enrichment that diversity brings to our organization and are committed to a culture that creates a sense of inclusion and belonging. We welcome new perspectives and affirm that all employment decisions are made without regard to race, color, ancestry, religion, national origin, age, familial or marital status, sex, sexual orientation, pregnancy, gender identity or expression, disability, genetic information, veteran status, or any other classification protected by federal, state, or local law. OVERVIEW: We are looking for an Analytics Engineer to join our Data Modeling & Governance Team! As an Analytics Engineer within the Data Modeling & Governance team, you will have the opportunity to work with a team responsible for building and maintaining scalable, reliable, and well-governed data models that support analytics and business decisions across the company. Your work will help ensure that data is accurate, consistent, secure, and ready to be consumed by Analytics, Product, Sciene, and other teams across the organization. REQUIREMENTS: Advanced SQL skills, including complex queries, CTEs, window functions, aggregations, joins, and data validation. Strong understanding of data modeling concepts, data structures, and data quality principles. Experience using Claude or similar AI tools to support data analysis, SQL development, documentation, and day-to-day workflows. Knowledge in Excel, including formulas, data analysis, lookups, pivot tables, and data validation. Basic knowledge of Databricks, including working with notebooks, tables, catalogs, and running SQL queries. Strong problem-solving skills, with the ability to investigate data issues, identify root causes, and propose effective solutions. Strong analytical and critical-thinking skills, with the ability to evaluate data, identify inconsistencies, and make well-supported conclusions. Strong attention to detail, particularly when working with data accuracy, consistency, and validation. PREFERRED QUALIFICATIONS: Experience working with e-commerce data and KPIs, such as Sales, Revenue, Orders, Conversion Rate, ROAS, ACOS, TACOS, CPC, and other commerce and advertising metrics. Experience with data from major e-commerce and advertising platforms, such as Amazon, Walmart, Shopify, Google Ads, Meta, or similar platforms. Familiarity with data governance, data quality monitoring, documentation, and standardization practices. Experience working with large and complex datasets. Experience collaborating with Analytics, Engineering, Product, or other cross-functional teams. WHAT YOU’LL DO: Perform business and data analysis to support internal initiatives and external/client-driven requests. Investigate and analyze data issues and bugs, identifying root causes and working with the appropriate teams to drive resolution. Proactively identify data quality issues, inconsistencies, and potential problems across our data models and services, ensuring they are properly addressed. Ensure the quality, accuracy, and reliability of our data services and deliverables through continuous validation and monitoring. Review and maintain clear, accurate, and up-to-date documentation for data models, business rules, metrics, processes, and services. Validate business rules, KPIs, and data transformations to ensure they accurately represent business requirements and expected behavior. Contribute to data governance and standardization, helping ensure consistent definitions, structures, naming conventions, and data quality standards across the organization. This is a PJ contract based in Brazil.