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3 semanas atrás
Our organization is dedicated to providing technical expertise to the global food and agricultural industry, ensuring safety, quality, and sustainability across international supply chains.
This role plays a critical part in building, maintaining, and modernizing data pipelines that process large-scale regulatory data from around the world and transform it into usable datasets for downstream applications and APIs.
The engineer will work hands-on with Python, SQL, and related tools to untangle legacy code pipelines, migrate processes to more maintainable platforms, and ensure accurate and reliable data. This role requires strong technical ability and a consulting mindset.
Key Responsibilities:- Designing and developing scalable data pipelines using Python and related tools.
- Migrating legacy pipelines to more maintainable platforms such as Airflow.
- Ensuring data accuracy, reliability, and readiness for client-facing products.
Our ideal candidate will possess the following qualifications:
- 7+ years' experience using Python for analyzing, extracting, creating, and transforming large datasets.
- Proficiency in Python 3+, Pandas, NumPy, and Jupyter Notebooks.
- Deep experience with SQL and relational data using Oracle, Postgres, or MS SQL Server.
- Solid understanding of database design principles, data modeling, and data warehousing concepts.
- Excellent troubleshooting skills and instincts.
While not required, the following qualifications would be advantageous:
- NoSQL database design and development using MongoDB, AWS DynamoDB, or Azure Cosmos DB.
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and services related to data storage/processing.
- Exposure to Terraform or other Infrastructure-as-Code tooling.
- Proficient in Azure DevOps for source code and pipeline management.
As a self-motivated and self-directed Data Engineer, you will thrive in an Agile software development team with short, iterative delivery cycles.
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