Credix is a FinTech company dedicated to growing businesses in Latin America. Building on our expertise, we now focus on providing a tailored Buy Now, Pay Later (BNPL) solution for B2B transactions in Brazil with our platform, CrediPay. CrediPay is created to help business grow their sales and improve their cashflow efficiency through seamless and risk-free credit offering. Sellers offer their buyers flexible payment terms at an attractive price point and receive upfront payments. We manage and protect our clients from any credit & fraud risk, letting them focus only on what matters: increased sales and profitability.
Learn more about our team, culture, and vision on our company page.
Why choose Credix?
As a Senior Data Engineer, you will be at the heart of Credix's data strategy, designing and building scalable pipelines and infrastructure that empower teams across the company. Your work will enable the Risk team to enhance predictive modeling, streamline data consumption for other departments, and help drive contextual underwriting and data-driven decision-making. You are passionate about leveraging data to solve complex challenges and revolutionize the B2B credit market in Brazil.
Fluent in Portuguese and English, both written and spoken.
Hands-on experience building ETL/ELT pipelines with dbt (must-have) and orchestration tools like Apache Airflow, Cloud Composer, or similar.
Deep understanding of Google Cloud Platform services (e.g., BigQuery, Cloud Storage, Cloud Run, Dataflow).
Expertise in SQL and Python, with clean, well-documented coding practices.
Familiarity with data warehousing best practices, medallion design, and analytics engineering principles.
Experience working with Terraform or similar IAC tools for provisioning data infrastructure.
Bonus: Experience with streaming data ingestion (e.g., Pub/Sub, Kafka, or Dataflow).
Bonus: Familiarity with financial services data (installments, receivables, delinquency, credit scoring, etc.) and regional data sources in Brazil (Serasa, Receita, CNPJ enrichment).
Proactive, detail-oriented, and self-motivated, with a strong commitment to quality and delivery.
Ability to clearly communicate data design trade-offs and mentor junior engineers or analysts in best practices.
Build and Own Ingestion Pipelines: Design robust, modular pipelines to ingest structured and semi-structured data into Google Cloud Platform (GCP) environments.
Develop Clean, Analytics-Ready Layers: Use dbt to transform raw ingested data into curated datasets optimized for credit risk modeling and business intelligence consumption.
Operationalize the Data Lake: Mmanage the data lifecycle of our transactional data to support both real-time and historical querying needs.
Metrics & KPI Layer: Create a single source of truth for key business KPIs and credit risk metrics by building reliable and tested data marts.
Implement Data Quality Controls: Deploy automated testing frameworks (e.g., dbt tests, GCP dataplex) to ensure 90%+ coverage and detect schema drift, nulls, and outliers.
Support API & 3rd Party Integrations: Develop ingestion frameworks for external APIs to enrich risk data.
Collaborate Across Functions: Work closely with Credit Risk, Operations, and Product teams to understand analytical needs and translate them into scalable data solutions.
Contribute to Platform Scalability: Design pipelines with reusability and modularity in mind to support onboarding new data sources and future expansion across regions or products.
Maintain Observability: Ensure logging, monitoring, and alerting are implemented across data flows for reliability and debugging (e.g., via GCP Logging, Cloud Monitoring, or third-party tools).
Documentation & Demo Ownership: Create clear, user-friendly documentation and visual diagrams of the data architecture and transformation layers.