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.