***About Us*** We are part of one of Magalu’s strategic pillars and internally develop the payment technologies for our ecosystem. Over the past few years, we have grown exponentially and become one of the largest companies in the sector. We offer solutions such as: * Digital accounts; * Receivables management; * Payment splitting; * Withdrawals and transfers; * Acquiring, credit cards, lending, Pix, and much more! Our purpose is clear: **to digitize businesses and promote financial inclusion—especially among Brazilian entrepreneurs.** As a Senior/Principal Data Engineer at MagaluPay, you will be a key contributor to building the data foundation that supports our decisions in credit, fraud, growth, operations, and product. Our Decision Sciences chapter (Business Analysis, Data Science, and Data Engineering) exists to transform real-world data—often chaotic and fragmented—into decisions that drive the business. Data Engineering is the backbone of this vision. We are responsible for the platforms, pipelines, and data products that empower MagaluPay analysts, data scientists, and engineers to deliver models, insights, and features with confidence. In this role, you will own end-to-end complex technical scopes—designing, building, and operating critical data solutions within a large-scale, continuously evolving environment. This is a highly technical individual contributor position. You will operate with a high degree of autonomy—making architectural and implementation decisions, solving difficult data problems in an enterprise-grade system, and helping define our engineering standards—without formal people management responsibilities (though this may represent a natural career progression path for this role). ### **Responsibilities** * Design, build, and operate end-to-end data pipelines—from ingestion through modeling, orchestration, and consumption—for high-impact domains such as credit, fraud, growth, and operations. * Own core components of our data platform (ingestion frameworks, transformation layers, data models, observability), continuously improving reliability, performance, and usability. * Model data to make it easy, secure, and efficient for use by analysts and data scientists—including building “trusted” layers with rigorously enforced high standards (dimensional models, data marts, feature tables, etc.). * Implement and maintain data quality, observability, and lineage: designing and implementing lineage-driven tests, monitoring, alerts, and runbooks to help teams trust the data, understand its origins, and respond rapidly to incidents. * Make architecture and tooling decisions that balance performance, cost, resilience, and simplicity—applying modern data warehouse/lakehouse patterns, medallion architectures, and best practices from the modern data stack. * Partner closely with Data Science, Business Analysis, Product, and Engineering teams to deeply understand use cases and translate them into robust, scalable data solutions. * Bring structure to ambiguous or poorly defined problems: clarifying requirements, mapping the current state, proposing alternatives, and guiding teams toward pragmatic, incremental solutions. * Serve as a technical reference within the Data Engineering chapter: reviewing designs and pull requests, sharing best practices, and helping evolve our engineering standards. ### **What We’re Looking For** * A strong track record working as a Senior or Principal (or equivalent) Data Engineer, owning complex technical scopes in production environments. * Deep expertise in modern data engineering practices, including: * + Advanced SQL and solid foundations in data modeling (dimensional/star schema, normalized models, slowly changing dimensions, etc.). + Experience with at least one major cloud provider (AWS, GCP, or Azure). + Hands-on experience with modern data warehouse / lakehouse technologies (Snowflake, BigQuery, Redshift, Databricks, or similar). + Familiarity with transformation/orchestration tools (dbt, Spark, Airflow, Prefect, Dagster, or equivalents). + Experience with AWS and tools such as Spark, dbt, and lakehouse table formats like Iceberg is a strong plus. * Proven ability to design and maintain production pipelines—including monitoring, alerting, and incident response. * Comfort operating with incomplete information and stabilizing systems not originally designed to be perfect from day one. * Systems thinking and strong communication: you understand how data models, infrastructure, tools, and user needs interconnect—and can explain your decisions to both technical and non-technical audiences. * A mindset aligned with how we work at MagaluPay: ownership, collaboration, pragmatic innovation, and a focus on enabling better decisions through better data. ### **Qualifications** * Typically 5–8+ years of experience in Data Engineering or a closely related field, with demonstrated growth in technical scope and responsibility throughout your career. * Proven experience with modern data platforms (Snowflake/BigQuery/Redshift/Databricks or similar) and orchestration/transformation frameworks (dbt, Spark, Airflow/Prefect/Dagster, etc.). * Proficiency in SQL and at least one programming language commonly used in data engineering (Python preferred; Scala/Java are pluses). * Experience in fintech, payments, or financial services is a plus—especially with use cases related to credit or risk. * A Bachelor’s degree in Computer Science, Engineering, Mathematics, or related fields is a plus—but equivalent practical experience is also valued. * Proficiency in both Portuguese and English; we operate in a bilingual environment, and many artifacts and interactions occur in both languages. **Have questions? Reach out to our R&S team.** ***Work Model: REMOTE*** *“If you seek a growth-oriented, challenging environment, ***Come Be Happy!***”* Learn more about us: www.magalupay.com.br If this opportunity resonates with you, apply now and join us in transforming the market! **We value diversity and inclusion across all our roles. At MagaluPay, everyone is welcome—regardless of gender, race, sexual orientation, disability, or any other characteristic.**