Data Engineer

Data Engineer at emagine

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 an innovative, customer-focused brand, leveraging the latest in data engineering best practices.

Responsibilities

• 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.
• 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.
• 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.
• 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.

Key skills/competency

• PySpark
• Spark SQL
• Databricks
• Delta Live Tables
• Azure Data Lake Storage
• Terraform
• GitOps
• ETL/ELT
• Data Modeling
• Apache Airflow