Job Summary: AgRisk is seeking a Senior Data Engineer to design and evolve a robust data platform using GCP and Databricks, build data pipelines, define architectural standards, and ensure data quality and observability within the agricultural credit context. Key Highlights: 1. Act as a technical reference and support the engineering team’s growth. 2. Design and evolve the data platform using GCP + Databricks. 3. Build and operate batch and near-real-time data pipelines. AgRisk is a **complete intelligence HUB** that **integrates data, technology, governance, and analytics** to support every stage of the agricultural credit journey. As a pioneering solution in the sector, it sets a new standard for efficiency, simplicity, and precision—making decisions faster, safer, and more consistent for **risk, credit, compliance, and intelligence teams.** **Core Hub Solutions:** * **AgRisk:** Integrates national restrictive data into the credit analysis workflow, enabling structured reading of delinquency, payment history, and risk signals throughout the decision-making journey. Information is consolidated into a single environment and used to support more consistent credit decisions—always alongside financial, legal, and behavioral data. * **AgFlow:** A credit pipeline responsible for structuring and organizing the credit analysis and decision-making journey—from onboarding to formalization. * **CredAcademy:** Training for professionals seeking authority over agricultural credit. **Your Responsibilities:** * Design and evolve the data platform using **GCP \+ Databricks**, ensuring scalability, reliability, and cost control; * Build and operate **batch and near\-real\-time** data pipelines for ingestion from internal and external sources; * Define data architecture and modeling standards (e.g., **lakehouse Bronze, Silver, and Gold**); * Build and evolve **data marts and analytical models** for product, revenue, risk, collections, and operations; * Implement **data quality and reliability practices** (validations, incremental processing, reprocessing, auditing); * Implement **data observability** (monitoring, alerts, SLIs/SLOs, and runbooks); * Optimize pipeline and query performance and cost; * Collaborate closely with Product, Engineering, Risk, and Analytics teams to align priorities and requirements; * Act as a **technical reference**, defining standards, reviewing PRs, and supporting team growth. **What We Expect From You:** * Proven experience as a **Senior Data Engineer** in a product environment; * Advanced **SQL** and **Python**, applied to data pipelines and transformations; * Experience with **Spark / Databricks** or equivalent distributed processing frameworks; * Strong hands-on experience in **analytical modeling** and data mart development; * Experience with **SQL and NoSQL databases** (PostgreSQL, MongoDB, or equivalents); * Experience with **cloud platforms** (preferably GCP), including security, cost management, and operations; * Engineering best practices: version control, code review, CI/CD, testing, and documentation; * Ability to make sound technical decisions, balancing trade-offs and business impact. **Nice-to-Haves:** * Experience with **CDC** and event-driven architectures; * Experience with **dbt**, data quality, and governance; * Exposure to **Machine Learning** use cases; * Technical/conversational English.