[Job - 29349] Senior Data Developer (AWS), Brazil

Design and build end-to-end data pipelines across the RAW, Silver, and Gold layers of the Medallion Architecture, ensuring reliability, performance, and maintainability at each stage

Architect data ingestion, transformation, standardization, and serving processes, structuring data flows from diverse and heterogeneous sources into a coherent analytical foundation

Model data for analytical consumption following Data Warehouse best practices, including Star Schema design and dimensional modeling suited for business intelligence and AI-readiness

Identify, evaluate, and consolidate new data sources relevant to agronomic business objectives, proactively engaging stakeholders to understand, obtain, and validate data availability and quality

Interact with business stakeholders and client leadership to translate domain requirements into data architecture decisions, challenging assumptions and proposing solutions grounded in technical evidence

Manipulate, optimize, and serve data in multiple formats — including Parquet, CSV, and geospatial datasets — tailored to the consumption needs of downstream AI applications and map-based visualizations

Manage and configure cloud infrastructure end-to-end, including storage, compute, access control, serverless functions, data cataloging, and event-driven processing on AWS

Own deployment and CI/CD practices for data pipelines — including repository management, branching strategy, test gates, and automated deploy workflows via GitLab

Support the creation of the data layer that will feed AI/ML applications, ensuring data quality, structure, and availability meet the requirements of machine learning workflows — without directly developing the models themselves

Operate as a proactive technical partner in a greenfield environment: question, propose, experiment, and iterate with the team rather than execute in isolation

English proficiency at B2 level or above — ability to explain technical flows, engage in discussions, ask clarifying questions, and collaborate effectively with international stakeholders (accent is not a barrier; communication clarity is)

Solid hands-on experience with AWS, covering the full infrastructure spectrum: S3, IAM (permissions and security configuration), Redshift, Lambda (serverless use cases), and Glue (including Glue Catalog for metadata management); ability to evaluate trade-offs between services for different pipeline scenarios

Experience with Terraform or equivalent Infrastructure-as-Code (IaC) tooling, applied recurrently in real data engineering projects — not just theoretical knowledge

Proficiency with GitLab for source control, CI/CD pipeline configuration, deployment workflows, and test gate management — specifically GitLab, not just generic Git experience

Strong proficiency in SQL, including complex query writing, analytical transformations, and performance tuning for data warehouse environments

Strong proficiency in PySpark, applied to large-scale distributed data processing — including partitioning strategies (e.g., by day/month/year), volume handling (tens to hundreds of GB), and performance optimization

Experience with Databricks, used in the context of data engineering pipelines and lakehouse architectures, including migration and deployment scenarios

Analytical data modeling expertise, with solid knowledge of Star Schema and dimensional modeling applied to data warehousing and business intelligence environments

Hands-on experience with the Medallion Architecture (RAW / Silver / Gold layers), including manipulation and optimization of Parquet and CSV files

Experience integrating and consolidating data from multiple heterogeneous sources, ensuring consistency, traceability, and analytical readiness

Mindset suited for greenfield projects: proactive, solution-oriented, comfortable with ambiguity, and able to contribute to architectural decisions — not just execute predefined tasks

Familiarity with SnapLogic or equivalent low-code/no-code ETL orchestration platforms (e.g., Pentaho, Airflow, Alteryx) — SnapLogic is the current standard at the client, with migration underway; hands-on experience with block/flow-based ETL logic is a differentiator

Experience with geospatial data processing and analytical environments focused on map-based and geographic visualization

Knowledge of DuckDB for in-process analytical queries

Background in data projects applied to agribusiness or precision agriculture

Exposure to predictive modeling workflows (e.g., gradient boosting, ensemble methods, or similar) — as a data provider to ML pipelines, not as a model developer