Senior Data Engineer

As a Senior Data Engineer at Calendly, your main responsibilities include designing and building batch and streaming data pipelines, mentoring fellow engineers, and ensuring the reliability and observability of our data infrastructure. You will work closely with product and engineering teams to meet evolving data needs and maintain high standards across the data platform.

To qualify as a Senior Data Engineer at Calendly, you should have over 5 years of experience in managing enterprise-grade cloud data warehouses such as BigQuery or Snowflake, and 2+ years with streaming and messaging systems like Apache Beam or Kafka. Expertise in SQL and Python is essential, and mentoring experience is a plus too.

As a Senior Data Engineer at Calendly, you can expect a supportive onboarding process where you'll learn about our data platforms and technologies, engage with your team, and get acquainted with our architectural practices. You'll be paired with mentors who will help you integrate smoothly into our engineering culture.

In the role of Senior Data Engineer at Calendly, you will be working with modern technologies such as Apache Flink, Google Cloud Platform, Kubernetes, and tools like Datadog and Airflow. Your technical expertise in SQL and Python will be crucial in designing scalable and reliable data solutions.

Calendly is committed to the career growth of its Senior Data Engineers by fostering a culture of continuous improvement and providing opportunities for mentorship, learning, and professional development. You'll have access to resources and flexible project opportunities that can accelerate your career trajectory.

When answering this question, highlight specific projects where you have implemented batch or streaming data pipelines, discuss the technologies you used such as Apache Beam or Flink, and detail the impact your solutions had on data accessibility and performance.

Discuss the strategies you employ to build robust monitoring and alerting systems, such as implementing self-healing mechanisms and conducting thorough testing. Make sure to mention the tools and practices that facilitate data reliability, such as strong logging practices and performance metrics.

Share your experiences mentoring junior engineers or peers, emphasizing how you guided them in technical aspects and fostered a supportive team culture. Provide examples of situations where your mentorship led to improvements in team performance or individual skill development.

Frame your answer around a specific instance where you confronted a significant challenge in your data engineering work. Detail your approach, the technologies you chose, why you made those decisions, and the outcome. This showcases your problem-solving skills along with your technical expertise.

Mention the resources you follow, such as blogs, podcasts, or conferences, and outline how you integrate new knowledge into your work. This shows your commitment to lifelong learning and adaptability in a fast-evolving field.

Lay out your hands-on experience using SQL for data manipulation and analysis. Highlight specific instances where you used SQL to optimize queries, aggregate large datasets, or enhance data extraction processes, showcasing the impact of your contributions.

Articulate the importance of data governance in ensuring data quality, compliance, and security. Discuss how you've contributed to implementing data governance frameworks in previous roles and the benefits these brought to data management practices.

Discuss your hands-on experience with cloud platforms like Google Cloud, AWS, or Azure, detailing how you utilized their services for data storage, processing, and analytics. Discuss any specific projects or outcomes that reflect your expertise.

Talk about your passion for data engineering and how solving complex problems excites you. Discuss how you seek challenges, engage in continuous learning, and share insights with your peers to contribute positively to the team landscape.