Senior Data Engineer

As a Senior Data Engineer at our company, you will be responsible for designing and developing end-to-end ETL data pipelines, implementing the Medallion architecture, and enhancing data accessibility by leveraging Azure Data Lake Storage. You will also transform raw data using Azure Data Factory and maintain close collaborations with data scientists and analysts to optimize data transformations.

To thrive as a Senior Data Engineer, you should have a Bachelor's degree in Computer Science or a related field, along with over 8 years of experience in business intelligence applications and ETL processes. Proficiency in Azure technologies, Python, SQL Server, and tools such as SSIS and Power BI is crucial for success in this position.

The tech stack for our Senior Data Engineer role includes Azure Data Lake Storage (ADLS) Gen2, Azure Data Factory, Azure Databricks, and Pyspark. Additionally, familiarity with Azure SQL, Azure Key Vault, Azure Synapse Analytics, and CI/CD pipelines via Azure DevOps are essential components of our infrastructure.

As a Senior Data Engineer, you will engage in various exciting projects focused on data pipeline development, analytics transformations, and data accessibility improvements. You'll be instrumental in implementing robust ETL solutions, automating data workflows, and collaborating on data-driven insights with cross-functional teams.

We are committed to nurturing talent and providing growth opportunities. As a Senior Data Engineer, you will have access to continuous learning programs, mentorship from experienced colleagues, and the chance to lead innovative projects, allowing you to grow your expertise and advance within the organization.

In my previous roles, I've designed and implemented comprehensive ETL workflows using tools like Azure Data Factory and SSIS. My focus has been on ensuring smooth data extraction, transformation, and loading while optimizing performance and data quality.

To maintain high data quality, I implement validation checks at various stages of the ETL process, utilize data profiling techniques, and regularly collaborate with data analysts to address any inconsistencies directly.

Performance tuning is critical for ETL processes. I often analyze query execution plans, optimize database indexes, and partition data intelligently to enhance load times and streamline processing efficiency.

In my past projects, Azure Databricks served as a powerful analytics platform. I utilized it for transforming data across layers, creating Delta tables for consistent data access, and facilitating collaborative development among teams using notebooks.

I prioritize open communication with data scientists and analysts to understand their requirements and ensure that the data engineering solutions I provide support their analytics needs effectively. Regular meetings and team discussions help maintain alignment.

I have integrated Azure DevOps for CI/CD pipelines in various projects, which streamlined my deployment processes. This included automating tests, managing version control, and deploying data engineering workflows for continuous improvement.

In troubleshooting a failed ETL job, I typically start by reviewing logs and error messages. I focus on identifying the root cause, whether it's a data issue, a coding error, or a configuration problem, and then I make the necessary adjustments to prevent reoccurrence.

Data security is a priority in my data engineering practices. I ensure sensitive information is encrypted, access controls are established through Azure Key Vaults, and compliance with data governance policies is enforced to protect organizational data.

To prioritize tasks, I assess deadlines, project importance, and team dependencies. I often use agile methodologies to manage workloads effectively and ensure that the most critical projects receive attention first.

I prefer using Power BI for data visualization due to its flexibility, ease of use, and seamless integration with Azure services, which allows me to create interactive dashboards and share insights effectively with stakeholders.