• Serve as a principal software engineer for AdSmart products • Architect and develop mission-critical backend services using microservices, serverless, and event-driven patterns under the leadership of the VP of Engineering • Participate in scrum ceremonies and perform peer code reviews • Utilize cutting-edge cloud computing technologies to solve problems • Drive integration of LLMs, AI agents, vector search, and ML-based personalization • Supporting products with the overall roadmap and providing updates to senior leadership

Job DetailsJob Location: Brazil - State/ProvinceAbout the role: Please submit a resume in English and respond to application questions to be considered. The Senior Data Engineer delivers the data strategy that powers analytics at Acadia, developing and implementing solutions that support data analysis, machine learning, and other advanced analytics techniques. They are responsible for building and maintaining ETL/ELT processes and data warehouses to ensure data is accurate, performant, and reliable for client projects and analyses. The role also includes automating procedures for client reporting and data exports, as well as extracting, transforming, and loading datasets from various sources. The Senior Data Engineer works closely with Senior, Mid, and Associate Engineers/Developers, as well as Business Intelligence and Data Science teams. Responsibilities: Building and maintaining client data warehouses - within cloud-based environments Develop, optimize, and maintain ETL/ELT pipelines (e.g., dbt, Azure DevOps) Maintains company procedures for change control and database project tracking and reporting Develop, implement and maintain application layer data protection solutions to ensure the security and privacy of Acadia’s confidential data Functions as a data owner and assigns database level access privileges Supporting analytics and modeling with Business Intelligence and Data Science teams Developing automated procedures to support overall analytics projects Researching potential solutions for future implementations Staying up to date on the latest cyber security threats through company provided training Review queries and/or database updates for quality assurance Database documentation (e.g., data dictionaries, ETL/ELT documentation) QualificationsRequirements: 5–8 years of experience in database development or data engineering Hands-on experience with Snowflake and dbt Advanced proficiency in SQL language and solid experience with Python Demonstrated experience in database and ETL/ELT design and implementation Experience working in AWS environments; Airflow experience preferred Strong attention to detail and commitment to data quality Excellent problem-solving and critical thinking skills Knowledge of media/marketing data (e.g., ad platforms, impressions, clicks, conversions) and how it flows into reporting and analytics is preferred Fluency in English is a must Please submit a resume in English and respond to application questions to be considered.

• Analyze user behavior, product performance, and key business metrics to identify trends and opportunities. • Create and maintain interactive dashboards and reports using tools like Tableau, Looker, Power BI, or Google Data Studio. • Collect, clean, and structure large datasets to ensure accuracy and usability. • Define and track KPIs for marketing, product, and business performance. • Work with SQL, Python, or R to extract, analyze, and manipulate data. • Collaborate with product managers, engineers, and marketing teams to provide data-driven insights. • Conduct A/B testing and experiment analysis to improve product features and user engagement. • Identify bottlenecks and opportunities in user journeys and customer funnels. • Present findings in a clear and compelling way to stakeholders and leadership teams. • Stay up to date with industry trends, analytics best practices, and emerging data tools.

• Analyze user behavior, product performance, and key business metrics to identify trends and opportunities. • Create and maintain interactive dashboards and reports using tools like Tableau, Looker, Power BI, or Google Data Studio. • Collect, clean, and structure large datasets to ensure accuracy and usability. • Define and track KPIs for marketing, product, and business performance. • Work with SQL, Python, or R to extract, analyze, and manipulate data. • Collaborate with product managers, engineers, and marketing teams to provide data-driven insights. • Conduct A/B testing and experiment analysis to improve product features and user engagement. • Identify bottlenecks and opportunities in user journeys and customer funnels. • Present findings in a clear and compelling way to stakeholders and leadership teams. • Stay up to date with industry trends, analytics best practices, and emerging data tools.