• Drive architectural strategy discussions with client leadership to ensure data platform initiatives align with business objectives and deliver measurable value • Act as the senior technical voice in client engagements, translating complex architectural decisions into business outcomes for both technical and executive audiences • Manage client relationships from an architectural perspective, building trust through technical excellence and strategic insight • Lead cross-functional teams of data engineers, data scientists, and analytics specialists in designing and deploying scalable, cloud-native data platforms • Support teams in planning and execution, providing architectural guidance and removing technical blockers throughout the delivery lifecycle • Mentor team members on cloud architecture best practices, data modeling principles, and emerging technologies • Design and implement robust AWS-based data lake architectures using Medallion (Bronze/Silver/Gold) patterns, managing trade-offs in storage strategies, partitioning schemes, and schema evolution • Lead the transition of legacy data structures and systems to modern cloud platforms, developing migration strategies that minimize risk and maximize business continuity • Architect solutions that balance performance, cost, scalability, and maintainability across complex data ecosystems • Partner with Data & Analytics Managers and business stakeholders to translate business requirements into feasible, scalable architectural decisions • Define data models, integration patterns, and platform capabilities that directly support business use cases and analytics needs • Navigate complex, siloed data landscapes to design practical solutions that deliver value incrementally • Design data platforms that natively support AI and ML workloads, including feature engineering pipelines, feature stores, model training data preparation, and inference serving infrastructure • Architect MLOps capabilities as a core platform component, not an afterthought, ensuring seamless integration of machine learning lifecycle management • Implement solutions leveraging agentic AI technologies to optimize data management, automate data quality workflows, and enhance analytics capabilities • Advise on governance models (centralized vs. federated) appropriate to organizational structure and data maturity • Implement Data Mesh principles to enable domain-oriented ownership while maintaining platform-level standards and interoperability • Define data quality frameworks, metadata management strategies, and security/privacy controls that scale across distributed architectures • Integrate AI tools and methodologies into daily architectural work, demonstrating innovative approaches to design, documentation, and problem-solving • Stay current with emerging data and AI technologies, evaluating their applicability to client challenges and incorporating them into platform strategies

• Serve as a trusted technical advisor for clients, bridging business strategy and cloud architecture • Lead the design and implementation of cloud-native data architectures with an emphasis on AWS solutions • Manage client relationships, building trust through technical excellence and strategic insight • Lead cross-functional teams in designing and deploying scalable, cloud-native data platforms • Support teams during execution, providing architectural guidance and removing blockers • Mentor team members on cloud architecture best practices and technologies • Design and implement robust AWS-based data lake architectures • Transition legacy data systems to modern cloud platforms, developing migration strategies • Partner with stakeholders to translate business requirements into architectural decisions • Design platforms that support AI/ML workloads and integrate AI tools into architectural work

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery • Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features • Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly • Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams • Improve data quality and reliability through testing, observability, alerting, and validation frameworks • Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions • Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

Role Description We are looking for a Senior Data Engineer to design, build, and optimize scalable data platforms that support analytics, ecommerce, logistics, and marketing initiatives. This role will be responsible for developing robust data pipelines, improving the data infrastructure, and ensuring high-quality, reliable data solutions in an AWS environment. The ideal candidate has strong expertise in Python, SQL, Spark, Airflow, and cloud-based data architectures, along with excellent communication skills and a collaborative mindset. - Design, build, and maintain scalable data pipelines for analytics, ecommerce, logistics, and marketing. - Improve and maintain the company's data infrastructure and tooling, including Redshift/Snowflake, Airflow, and Fivetran. - Architect data processing systems capable of handling complex data flows and large-scale datasets. - Develop reliable, efficient, and cost-effective data solutions in an AWS environment. - Ensure high standards of data quality through automation and best engineering practices. - Optimize platform performance, resiliency, scalability, and operational costs. - Collaborate closely with software engineers, analysts, and business stakeholders. - Mentor engineers and analysts on data engineering best practices. - Participate in architecture discussions and technical decision-making. Qualifications - 4+ years of experience as a Data Engineer working with modern data technologies. - Strong proficiency in Python and SQL. - Experience processing large-scale datasets. - Hands-on experience with Apache Spark and Apache Airflow. - Experience designing and architecting data processing systems in AWS. - Experience with Redshift and/or Snowflake. - Knowledge of Fivetran or similar data ingestion tools. - Strong understanding of data quality, automation, and data engineering best practices. - Excellent communication and interpersonal skills. Requirements - Experience with streaming technologies such as Kafka or AWS Kinesis Firehose. - Knowledge of Docker and/or Kubernetes. - Familiarity with Ruby or R. - Experience building or migrating a Data Warehouse. - Experience leading projects or mentoring technical teams. Benefits - A stable, long-term contract with opportunities for career growth. - Private health insurance. - A remote-friendly culture that promotes work-life balance. - Continuous training, mentorship, and learning programs to keep you at the forefront of the industry. - Free access to AI training resources and state-of-the-art AI tools to elevate your daily work. - A flexible Paid Time Off (PTO) policy as well as paid holiday days. - Challenging, world-class software projects for clients in the US and LatAm. - Collaboration with some of the most talented software engineers in Latin America and the US, in a diverse work environment.