Senior Data Engineer - (100% Remote) Fully Remote: Work from anywhere with flexible hours that fit your lifestyle. Award-Winning Culture: Be part of a company recognized for exceptional employee satisfaction, inclusivity, and professional development. Competitive Compensation: Generous salary, performance bonuses, and comprehensive benefits package. Professional Growth: Access to mentorship programs, certifications, and opportunities to advance your career. Cutting-Edge Tech: Work with state-of-the-art tools and technologies on impactful, high-visibility projects. Key Responsibilities: Design and implement scalable, efficient data pipelines using Azure Data Lake , Databricks , Snowflake , and Synapse Analytics . Develop and optimize workflows with Apache Spark and Scala for batch and streaming data processing. Build, maintain, and enhance robust ETL/ELT pipelines tailored to big data applications within Azure ecosystems. Manage and optimize data storage solutions like Azure Data Lake Storage , Snowflake , and Synapse Analytics to ensure peak performance and cost-efficiency. Partner with data scientists, analysts, and business teams to ensure the reliability and availability of data platforms. Monitor and fine-tune the performance of data platforms in production environments. Enforce best practices for data governance, security, and compliance in Azure-based data frameworks. Stay ahead of the curve by researching and integrating new big data technologies to enhance scalability and performance. Requirements: Essential Skills and Experience: A minimum of 5 years of experience in data engineering or related fields. At least 3 years of hands-on expertise in Apache Spark using Scala . Advanced knowledge of Azure data services , including Azure Data Lake , Azure Databricks , Azure Synapse Analytics , and Azure Data Factory . Proficiency with Snowflake , including schema design, performance optimization, and integration with cloud platforms. Solid expertise in cloud-based big data solutions , particularly within the Azure ecosystem. Strong knowledge of data modeling , ETL/ELT pipelines , and database concepts . Experience with streaming platforms such as Spark Streaming , Kafka , or Event Hubs . Familiarity with data lake and data warehouse architecture (e.g., Delta Lake , Snowflake , Synapse ). Proficiency in DevOps practices, including CI/CD pipelines for data engineering workflows. Preferred Skills: Knowledge of Python for data engineering tasks. Experience with Azure Machine Learning or other machine learning platforms integrated with data workflows.
Experience: 5 years required