This position designs data products and data pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost effective. Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.
Responsibilities include, but are not limited to, the following:
- Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric).
- Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage).
- Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions.
- Optimize data pipelines in the Azure environment for performance, scalability, and reliability.
- Ensure data quality and integrity through data validation techniques and frameworks.
- Develop and maintain documentation for data processes, configurations, and best practices.
- Monitor and troubleshoot data pipeline issues to ensure timely resolution.
- Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge.
- Manage the CI/CD process for deploying and maintaining data solutions.
Required Qualifications:
- Bachelor's degree in computer science, Engineering, or a related field (or equivalent experience) and able to demonstrate high proficiency in programming fundamentals.
- Proven experience (7+ years) as a Data Engineer or similar role dealing with data and ETL processes.
- Strong knowledge of Microsoft Azure services, including Azure Data Factory, Azure Synapse, Azure Databricks, Azure Blob Storage and Azure Data Lake Gen 2.
- Experience utilizing SQL DML to query modern RDBMS in an efficient manner (e.g., SQL Server, PostgreSQL).
- Strong understanding of Software Engineering principles and how they apply to Data Engineering (e.g., CI/CD, version control, testing).
- Experience with big data technologies (e.g., Spark).
- Strong problem-solving skills and attention to detail.
- Excellent communication and collaboration skills.
