Job Description

• 5-8 years of overall work experience in relevant fields, demonstrating a solid background in data engineering.
• The work model is hybrid, allowing for a combination of in-office and remote work to accommodate team collaboration and productivity.

• Expert knowledge of retail and eCommerce, showcasing the ability to address specific industry challenges.
• Strong working knowledge of ETL processes, enabling effective data management and transformation.
• Design and develop end-to-end ETL / ELT pipelines for ingesting, transforming, and loading data from multiple sources
• Build and maintain cloud-based data pipelines on Azure, including orchestration, automation, and monitoring
• Design and implement data warehouse solutions, including data modeling for analytical use cases
• Apply strong SQL skills for data transformation, validation, and analytical processing
• Support downstream analytics and reporting platforms, including Power BI
• Exposure to modern data processing concepts and AI-assisted tools to enhance pipeline development and data workflows

• Demonstrated expertise in ETL tools and methodologies, ensuring efficient data workflow

• Implement data ingestion pipelines from diverse sources, ensuring large-scale structured and unstructured data transmission between systems.
• Develop data aggregation pipelines that effectively feed analytic and reporting solutions.
• Maintain scalable code that adheres to company standards and is thoroughly documented.
• Participate actively in agile SCRUM ceremonies, including refinement, planning, stand-ups, and retrospectives.
• Flushing out business requirements and adding acceptance criteria to user stories.
• Engage in peer review code sessions to uphold code quality.
• Communicate effectively with management regarding proposed solutions and challenges.
• Supervise and resolve data issues, demonstrating creative problem-solving skills.
• Maintain in-depth knowledge of the data ecosystem and trends, acting as a domain expert.
• Recommend new tools to enhance the current stack and efficiently address data needs.

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