Senior Data Engineer

• 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.