Senior Azure Data Engineer

As a Senior Azure Data Engineer at HSO Enterprise Solutions, your primary responsibilities include designing and developing data pipelines, building robust data models, and collaborating with data scientists and analysts to transition machine learning models into the production environment. You’ll also focus on optimizing data systems for performance and cost efficiency, ensuring data integrity through rigorous monitoring, and integrating solutions into client applications.

Applicants for the Senior Azure Data Engineer position at HSO Enterprise Solutions should possess a Bachelor’s degree in Computer Science or Data Engineering, along with at least 5 years of experience either in this role or as a Software Engineer. Familiarity with Azure technologies including Data Factory, Synapse, and data lakes, as well as skills in Python and data governance frameworks, are essential.

In the role of Senior Azure Data Engineer at HSO Enterprise Solutions, you will work with a variety of advanced technologies including Azure Data Factory, Synapse, Spark, Databricks, and various data visualization tools, ensuring that you leverage cutting-edge solutions for all data engineering processes.

Yes, HSO Enterprise Solutions offers the option to telecommute as a Senior Azure Data Engineer. We understand the importance of work-life balance and offer flexible working arrangements to suit your personal needs while ensuring you thrive in your role.

HSO Enterprise Solutions provides a comprehensive benefits package for Senior Azure Data Engineers, which includes competitive salary and performance-based bonuses, unlimited paid time off, medical, dental, and vision coverage, a 401(k) plan with company match, and additional health reimbursement accounts to ensure you maintain a healthy and fulfilling lifestyle.

When designing an efficient data pipeline, it's crucial to understand the data flow, identify bottlenecks, and determine the best techniques for data ingestion, transformation, and storage. Discussing your methodology integrating Azure tools, performance considerations, and how you ensure data quality can strengthen your case in an interview.

In your response, detail specific projects where you implemented Azure Data Factory for creating data pipelines, focusing on challenges faced, your role in resolving them, and the outcomes. Highlight your knowledge of ADF activities, triggers, and integration with other Azure services.

To ensure data quality and integrity, I advocate for implementing rigorous monitoring processes and validation checks throughout the data lifecycle. Discuss tools and methodologies you've employed, such as data profiling and reconciliation processes, while ensuring that stakeholders’ expectations are consistently met.

Highlight a specific instance where you assessed the performance of a data pipeline, identified limitations, and enumerated the steps taken to enhance its efficiency. Point out the tools used for monitoring performance and the measurable impact of your optimizations.

In this question, emphasize your commitment to continuous learning. Mention any conferences, online courses, or publications you follow. Also, showcase how you apply newly acquired knowledge to enhance process efficiencies or implement innovative solutions at work.

Discuss your knowledge of data governance and its importance in ensuring compliance and effective data management. Mention specific frameworks like Azure Purview, and your past roles in implementing governance protocols within data projects.

Provide examples of how you've worked with different teams such as data scientists, developers, and system architects, ensuring that communication and data requirements were met. Emphasize your ability to bridge gaps between technical and non-technical stakeholders.

Detail your hands-on experience with ETL processes and tools, such as Synapse, Spark, or even third-party platforms. Illustrate how you've developed and maintained ETL workflows with a focus on real-world applications and outcomes.

Discuss the principles behind designing reusable data extraction workflows, such as modular architecture and the use of templates or frameworks. Emphasize how this approach aids efficiency and maintainability for future projects.

In managing source control, I utilize tools such as Git and GitHub, stressing the importance of proper versioning and documentation. Discuss your strategy for collaboration and how you've handled code reviews and integration during team projects.