Senior Data Engineer

Health-E Commerce is seeking a Senior Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure supporting its data needs. The role focuses on data integration, modeling, quality, and performance using Azure data services, while partnering with analysts and cross-functional stakeholders.

Responsibilities:

• Design, develop, and maintain robust data pipelines and infrastructure to support data integration, processing, and storage
• Collaborate with analysts and other stakeholders to understand data requirements and translate them into technical solutions
• Implement best practices for data governance, security, and quality assurance to ensure the reliability and accuracy of data
• Explore and evaluate new technologies and tools to enhance our data infrastructure and capabilities
• Design and implement complex data models on Azure Synapse Analytics to support data warehousing Utilize Azure DevOps for continuous integration, continuous delivery, and agile project management
• Manage and optimize Apache Spark pools for high-performance data processing tasks
• Develop and maintain ETL processes and data pipelines using Python scripting
• Ensure data quality and integrity across all platforms and systems
• Collaborate with cross-functional teams to translate business needs into technical specifications
• Stay current with industry trends and advancements in Azure data services

Requirements:

• Bachelor's degree in Computer Science, Engineering, or related field
• Minimum of 5+ years experience in data engineering roles, with a strong focus on building and maintaining data pipelines and infrastructure
• Strong proficiency in Azure Synapse Analytics, Azure DevOps, and Apache Spark pools
• Expertise in Python scripting and other programming languages such as SQL and Java
• Senior level knowledge of data modeling, processes, and data warehouse concepts
• Strong communication skills and ability to effectively communicate technical concepts to non-technical stakeholders
• Experience with data modeling, warehousing, and building ETL pipelines
• Familiarity with Azure Data Factory, Azure Data Lake Storage, and other Azure data services