Data Engineer

As a Data Engineer at Sigma, your primary responsibilities include architecting and managing production data pipelines in Snowflake, optimizing ETL processes, and ensuring data governance across various Cloud Data Warehouses. You will work closely with teams across product, engineering, and analytics to build scalable processes that support both internal and external data use cases, making a significant impact on our data-driven culture.

To be a suitable candidate for the Data Engineer role at Sigma, you should possess a strong background in data engineering with at least 3 years of relevant experience. Proficiency in building data pipelines, familiarity with APIs, and experience in deploying data governance frameworks are pivotal. Strong communication skills and a collaborative spirit are vital as you will partner with various internal teams to drive data initiatives.

At Sigma, we believe in continuous learning and development. As a Data Engineer, you will have ample opportunities to expand your skill set, including working with advanced tools like dbt, Snowflake, and orchestration technologies like Airflow. We foster an environment where sharing knowledge and insights is encouraged, allowing you to stay updated with the ever-evolving data ecosystem.

In the Data Engineer role at Sigma, you will be predominantly using Snowflake and Databricks for data architecture and processing. Additionally, you'll work with contemporary tools and technologies such as Fivetran, dbt, Hightouch, and Metaplane, ensuring you are well-versed in best-in-class data engineering practices that drive impactful insights.

Absolutely! Sigma is committed to the professional development of its employees, and as a Data Engineer, there are numerous pathways for advancement. Whether you wish to deepen your technical expertise or take on more leadership responsibilities, you'll find opportunities for growth to match your aspirations and career goals.

When answering this question, be specific about the ETL tools you’ve used, such as Fivetran or dbt, and provide concrete examples of data pipelines you've built or optimized. Discuss the challenges you faced in these projects and how you overcame them, emphasizing your problem-solving skills and technical proficiency.

To address this, you should talk about the processes you implement, such as data validation techniques, establishing data governance frameworks, and regular monitoring procedures. Provide examples where your actions directly improved data accuracy and reliability.

When sharing a project, outline the objectives, your role, the challenges you faced, and how you achieved the deliverables. Highlight your technical skills and your collaboration with other teams to ensure cross-functional efficacy.

Outline your experience with cloud data platforms like AWS or GCP, including the services you’ve used and how you've implemented solutions in these environments. Be specific about any notable projects that highlight your expertise.

You can mention methods and resources you use to keep your skills sharp, such as online courses, webinars, industry blogs, or participating in communities. Demonstrating a proactive approach to learning showcases your passion for the field.

When answering, be detailed about the APIs you’ve worked with, how you integrated them into your data workflows, and any obstacles you encountered. Highlight your understanding of RESTful services and how you utilized tools to manage API requests.

Discuss how effective communication and teamwork are critical to a successful data engineering workflow. Give examples of cross-departmental collaboration you've experienced and how it enhanced project outcomes, emphasizing your interpersonal skills.

Be prepared to discuss specific techniques such as partitioning, indexing, and caching strategies that you've employed. Share results from previous optimizations to demonstrate the tangible impact of your efforts.

Explain the importance of documentation in data engineering and describe your methods for maintaining clear, accessible records of data pipelines and governance practices. Share any documentation tools you've used and how they benefited the team.

Discuss strategies you utilize to assess task urgency and importance, such as utilizing Agile methodologies or project management tools. Provide examples showing your ability to thrive in ambiguous situations and deliver results effectively.