Principal Data Engineer (Azure)

As a Principal Data Engineer (Azure) at Tiger Analytics, your primary responsibilities include designing and building scalable data ingestion pipelines, conceptualizing high-performance data processing for both structured and unstructured data, and scheduling, orchestrating, and validating pipelines. You'll also be expected to collaborate with various teams to make tech stack decisions and ensure that analytical solutions are delivered on time.

To qualify for the Principal Data Engineer (Azure) role at Tiger Analytics, candidates should have hands-on experience with Azure Data Factory, PySpark, and Databricks, alongside a solid understanding of big data technologies like Hadoop and Spark. Strong programming skills in SQL, Python, or Scala/Java are a must, along with experience working in Agile projects and familiarity with DevOps processes.

Yes, certifications like Data Engineering on Microsoft Azure (DP-203) and Databricks Certified Developer (DE) are highly valued and can enhance your application for the Principal Data Engineer (Azure) position at Tiger Analytics. These certifications demonstrate your commitment to professional development and expertise in the necessary technologies.

In the Principal Data Engineer (Azure) role at Tiger Analytics, you'll work with a variety of tools and technologies, including Azure Data Factory, Databricks, Azure SQL Database, and big data technologies like Hadoop, Spark, and Kafka. Additionally, you may engage with Azure Synapse Analytics, Cosmos DB, and various file formats such as Delta Lake and JSON.

The work culture at Tiger Analytics is characterized by a team-first mindset that values expertise, collaboration, and respect. You’ll be working in an environment that encourages innovative thinking and supports personal and professional growth. Being a Great Place to Work-Certified™ we aim to foster a positive and inclusive atmosphere where your ideas and contributions are welcomed.

In your response, focus on specific projects where you've implemented Azure Data Factory. Discuss the types of data ingestion pipelines you built, the challenges you faced, and how you resolved them. Highlight any metrics or improvements in efficiency you achieved due to your efforts.

When answering this question, outline your systematic approach to pipeline design. Talk about the importance of scalability, reliability, and maintainability. Include details about the tools and technologies you prefer, such as ADF or Databricks, and explain how you ensure data quality throughout the process.

Highlight your understanding of data security practices, including access controls, encryption, and compliance with data governance policies. Discuss how you work with BI and Data Science teams to ensure the secure consumption of data, and mention any specific strategies you implement to maintain governance throughout the data lifecycle.

In your answer, narrate a specific instance where debugging was a challenge. Describe the problem, the methodologies you employed to pinpoint the issue, and how you communicated the resolution to your team. Emphasizing your debugging skills in SQL, Python, or Scala/Java would be beneficial here.

Discuss current trends such as the rise of serverless architectures, the shift towards real-time data processing, and the increasing importance of data governance in cloud environments. Share how these trends may require new tools and methodologies, and how you adapt your practices to stay current in the evolving data engineering landscape.

Provide an example where teamwork was essential. Describe the project, your role within the teams, the communication methods you used, and how collaboration led to achieving the project goals efficiently. Highlight your ability to work with stakeholders from different backgrounds effectively.

Discuss your knowledge of data modeling techniques such as star schema, snowflake schema, or data vault. Give examples of how you applied these techniques in practice, detailing the results your modeling brought to various analytical solutions you've developed.

In your answer, detail specific projects or tasks where you utilized Hadoop or Spark. Discuss the problems you tackled, any optimizations you made, and the benefits these technologies brought to your data engineering processes.

Share a specific challenging project, emphasizing both the technical difficulties faced and the strategies you implemented to overcome them. Discuss the end result and any lessons learned that can be applied to your future work as a Principal Data Engineer.

Discuss your commitment to ongoing learning through methods such as reading industry publications, attending webinars, participating in online courses, or being active in professional communities. Share specific resources or certifications you've pursued that help you stay at the forefront of data engineering innovations.