• Work with senior business stakeholders to break down business problems and design analytics solutions. • Provide technical leadership to drive best practices for data modeling, ETL, and data quality. • Design and build data pipelines to enable decision-making through analytics. • Develop automated solutions to replace manual data processes. • Refine and enhance reporting data models. • Act as a champion for data quality by proactively identifying data quality issues and coordinating with system owners to resolve issues quickly. • Build relationships with senior business leaders to gather requirements and evangelize data analytics best practices.

• Define and evolve data architecture standards and best practices. • Design and implement Data Lake and Data Warehouse/Lakehouse architectures. • Architect data pipelines (ETL/ELT) for ingestion, transformation, and consumption. • Design data models aligned with analytics and business requirements. • Ensure data governance, quality, and security across platforms. • Define integration patterns between microservices and data systems. • Support delivery teams with architectural guidance and solution design. • Collaborate with stakeholders to translate business needs into scalable data solutions. • Contribute to cloud data architecture decisions across AWS, Azure, or GCP.

• Operate OneTrust to resolve data subject requests (DSARs) end-to-end. • Execute and follow up on the various compliance use cases related to data privacy. • Maintain and act on the data map/data inventory already built from the initial scan. • Collaborate with the data team to build automations between OneTrust and internal data sources (DynamoDB, Aurora, S3, Google Drive). • Ensure GDPR compliance across dLocal's internal databases. • Serve as the functional point of reference for OneTrust within the compliance team.

• Plan, create, and maintain data architectures, ensuring alignment with business requirements. • Obtain data, formulate dataset processes, and store optimized data. • Identify problems and inefficiencies and apply solutions. • Determine tasks where manual participation can be eliminated with automation. • Identify and optimize data bottlenecks, leveraging automation where possible. • Create and manage data lifecycle policies (retention, backups/restore, etc). • In-depth knowledge for creating, maintaining, and managing ETL/ELT pipelines. • Create, maintain, and manage data transformations. • Maintain/update documentation. • Create, maintain, and manage data pipeline schedules. • Monitor data pipelines. • Create, maintain, and manage data quality gates (Great Expectations) to ensure high data quality. • Support AI/ML teams with optimizing feature engineering code. • Expertise in Spark/Python/Databricks, Data Lake and SQL. • Create, maintain, and manage Spark Structured Steaming jobs, including using the newer Delta Live Tables and/or DBT. • Research existing data in the data lake to determine best sources for data. • Create, manage, and maintain ksqlDB and Kafka Streams queries/code Data driven testing for data quality. • Maintain and update Python-based data processing scripts executed on AWS Lambdas. • Unit tests for all the Spark, Python data processing and Lambda codes. • Maintain PCIS Reporting Database data lake with optimizations and maintenance (performance tuning, etc). • Streamlining data processing experience including formalizing concepts of how to handle lake data, defining windows, and how window definitions impact data freshness.