Azure Databricks Lead

ETL Data Engineer with Azure space

Charlotte, NC - Onsite

We are looking for Lead ETL Data Engineers, We specifically need senior profiles with strong expertise in Data Ingestion, Data Warehousing, and Data Analytics who are capable of leading migrations. Experience with Azure is preferred but not mandatory.

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.

Required Skills:

• 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

Preferred Skills:

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

Job Responsibilities

• 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.