Principal Data Engineer (Azure) - job 1 of 2

As a Principal Data Engineer at Tiger Analytics, you will be responsible for designing and building scalable and metadata-driven data ingestion pipelines. You will also execute high-performance data processing for both structured and unstructured data, schedule and orchestrate pipelines, and validate data processing tasks, ensuring they run smoothly. Collaborating with consulting, data science, and app development teams will be a key part of your role, as you'll bring analytical solutions to life effectively.

To qualify as a Principal Data Engineer at Tiger Analytics, you should have experience with Azure Data Factory, PySpark, Databricks, Azure Data Lake Storage, and Azure SQL Database. Additionally, familiarity with Azure Synapse Analytics, Event Hub, and various big data technologies is advantageous. Strong programming skills in SQL, Python, or Scala/Java, along with debugging and unit testing expertise, will be essential to excel in this role.

In the Principal Data Engineer role at Tiger Analytics, you will work with a wide range of technologies, primarily focusing on the Azure platform. Key technologies include Azure Data Factory, Databricks, Azure SQL Database, and Azure Synapse Analytics. You'll also work with big data tools like Hadoop, Spark, and Kafka, as well as files in formats like Delta Lake, Avro, and JSON, emphasizing cloud and open-source solutions.

Tiger Analytics fosters a culture that revolves around respect and teamwork. As a Principal Data Engineer, you will be part of an environment that encourages innovation and collaboration. The company prioritizes continuous learning and personal growth, empowering engineers to take ownership of their projects while actively participating in a supportive, client-focused atmosphere. Plus, being Great Place to Work-Certified™, employees experience a workplace that values their contributions.

As a Principal Data Engineer at Tiger Analytics, you will have access to numerous opportunities for career advancement. Our rapidly growing firm encourages individual responsibility and offers chances to work on diverse, challenging projects. With ongoing training options and pathways for progression into leadership roles, your career can flourish in an entrepreneurial environment that values your expertise and contributions.

In answering this question, highlight specific projects where you've used Azure Data Factory, elaborating on how you designed data pipelines and integrated various data sources. Discuss the challenges you faced and the solutions you implemented to enhance performance and reliability.

When responding, share specific examples of projects that required collaboration with data science teams. Emphasize the tools and strategies you used to facilitate communication, such as regular meetings or shared documentation, and underscore the importance of understanding their data needs.

Discuss your approach to writing code, focusing on modularity, code reviews, and best practices like unit testing. Mention any specific methodologies you've followed, such as Agile or DevOps, to ensure your code remains scalable over time.

In your answer, delineate structured vs. unstructured data, providing examples of each. Explain your experience with data processing techniques that accommodate both types, particularly in an Azure environment, showcasing your versatility as a Principal Data Engineer.

Provide an overview of the tools you've utilized for data orchestration, such as Apache Airflow or Azure Data Factory. Detail your rationale for choosing these tools based on project requirements, highlighting features that helped improve efficiency, reliability, or scalability.

Share your approach to task prioritization, focusing on how you assess project urgency and importance. Mention any techniques you utilize for project management, such as Kanban boards or Gantt charts, to stay organized and meet deadlines effectively.

When addressing this question, discuss specific projects where you implemented Data Lake solutions. Describe the architecture you designed, technologies used, and the impacts on data accessibility and analytics within your organization.

Reflect on any challenges you've encountered, such as scaling issues or performance bottlenecks, and explain how you addressed them. Highlight your problem-solving skills and adaptability in navigating the complexities of big data technology.

Express your commitment to continuous learning by mentioning specific sources you follow, such as blogs, podcasts, or online courses. Highlight any professional groups or networks you engage with to stay connected with advancements in data engineering.

In your response, emphasize the importance of the Principal Data Engineer in coordinating between teams, ensuring data integrity, and leading technical strategy. Discuss how your contributions ultimately drive project success through improved data solutions and collaboration.