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Data Engineer

Summary

Build and maintain scalable data pipelines for a fashion/home e-commerce brand using PySpark, Databricks, and Azure cloud services.

Industry: E-commerce (Fashion & Home)

Location: Portugal

Work Model: 100% Remote

Start Date: ASAP

Project Language: English

Project Overview:

Join a leading e-commerce company in fashion and home décor, as they undertake critical data engineering projects to support their evolving business needs. This is an exciting opportunity to contribute to high-impact data solutions for an innovative, customer-focused brand, leveraging the latest in data engineering best practices.

Responsibilities:

  • Project Understanding and Communication:

    • Analyze business and technical challenges from a user perspective.

    • Collaborate with Data Architects and Project Managers to ensure solutions align with the client´s data architecture.

  • Data Pipeline Development:

    • Design, build, and deploy efficient data pipelines according to project requirements.

    • Apply best practices for performance, scalability, and maintainability.

    • Use Terraform to deploy and manage infrastructure efficiently.

  • Testing and Deployment:

    • Define test cases and conduct testing in collaboration with the Project Manager.

    • Present completed developments to Data Architects and Lead DataOps, ensuring smooth deployment and active monitoring post-deployment.

  • Documentation and Peer Review:

    • Document processes, tests, and results thoroughly.

    • Conduct peer reviews and participate in code reviews for quality assurance.

Requirements:

Hard Skills:

  • Proficiency in PySpark and Spark SQL for data processing.

  • Experience with Databricks and Delta Live Tables for ETL and workflow orchestration.

  • Familiarity with Azure Data Lake Storage for data storage and management.

  • At least 1 year of experience with Terraform and GitOps practices for infrastructure deployment.

  • Strong understanding of ETL/ELT processes, data warehousing, data lakes, and data modeling.

  • Knowledge of orchestration tools (e.g., Apache Airflow) for pipeline scheduling and management.

  • Experience with data partitioning and lifecycle management in cloud storage.

Optional: Experience with Databricks Asset Bundles, Kubernetes, Apache Kafka, and Vault is a plus.

Soft Skills:

  • English Fluency: Strong written and verbal English skills in a working environment.

  • Communication: Ability to convey technical concepts effectively and understand user needs.

  • Organizational Skills: Detail-oriented with the ability to maintain structured documentation.

  • Problem-Solving: Proactive approach to understanding and addressing data challenges.

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