Lead Azure Data Engineer

As a Lead Azure Data Engineer at Derex Technologies, you will be responsible for designing and developing data pipelines, cleansing and transforming data from various sources, and loading it into Azure Data Lake Store and Synapse. You will also perform root cause analysis on data processes, apply automation techniques, and mentor junior Data Engineers, ensuring high-quality deliverables that meet client needs.

To qualify for the Lead Azure Data Engineer role at Derex Technologies, candidates should have a B.E./B. Tech degree in computer science or a related field, alongside 10-12 years of relevant experience. Proficiency in Azure Data Factory, Azure Data Lake Store, CosmosDB, and handling various data formats such as SQL, XML, and JSON is essential. Strong communication skills and experience in the healthcare or finance sectors are preferred.

At Derex Technologies, you can expect a dynamic and collaborative work environment that values innovation and agility. As a Lead Azure Data Engineer, you will work closely with both technical leads and business stakeholders to drive data solutions, while also fostering a culture of mentorship among team members.

The Lead Azure Data Engineer plays a crucial role at Derex Technologies by applying their in-depth business knowledge to identify challenges and create data-driven solutions. This role involves extracting insights from data that can lead to strategic decision-making and operational improvements for our clients in various industries.

While not mandatory, having experience with Big Data technologies such as Databricks and Spark libraries can enhance your candidacy for the Lead Azure Data Engineer position at Derex Technologies. Certifications like Microsoft Azure Data Engineer Associate and exposure to sectors such as Banking, Mortgage, or Insurance are also advantageous.

When discussing your experience with Azure Data Factory, highlight specific projects where you designed data pipelines. Talk about the tools and techniques you utilized, any challenges you faced, and how you overcame them. Providing measurable results will strengthen your response.

Emphasize the importance of data validation and cleansing procedures in your answer. Discuss specific methodologies you use, such as automated testing and data profiling, to maintain high-quality standards in the datasets you handle.

Outline best practices such as using normalization and denormalization as appropriate, maintaining data integrity through constraints, and optimizing for performance. Consider sharing an example of how you've implemented these best practices in a past project.

When answering this question, focus on a specific project where you identified a significant problem, the steps you took to analyze it, and the solution you implemented. Highlight how it impacted the business positively while detailing your methodical approach.

Discuss your systematic approach to debugging, including verifying data at each stage of the pipeline, using logging to identify where errors occur, and collaborating with other team members if necessary. Mention specific tools you might use for monitoring.

It's vital to illustrate your communication skills here by referring to a specific instance where you simplified technical jargon into relatable terms. Share how you ensured that all stakeholders understood the implications and possible solutions.

Talk about your preferred data visualization tools, such as Power BI, and explain your choices based on user experience, functionality, or integration capabilities. Providing examples of visualizations you've created and their impact can demonstrate your expertise.

Discuss various ways you keep yourself informed, such as following industry blogs, participating in online courses, attending seminars or webinars, and engaging with professional networks. Emphasize your commitment to continuous learning in the tech field.

Highlight your understanding of data security principles, such as encryption, access control, and monitoring. Share specific instances where you implemented security measures within data pipelines or while working with Azure services.

Explain your approach to prioritization based on urgency, project impact, or deadlines. Mention tools or techniques you use to manage your time effectively, such as Kanban or Scrum methodologies.