Data Engineer

As a Data Engineer at Seesaw, your primary responsibilities include designing and implementing scalable data warehouse models, evolving ETL pipelines, and collaborating with internal stakeholders to understand business requirements. You'll also ensure data accuracy and optimize pipeline performance, all while contributing to the growth of our international data infrastructure.

To qualify for the Data Engineer position at Seesaw, candidates should have a Bachelor’s or Master’s degree in Computer Science or a related field along with at least 4 years of experience in data engineering. Proficiency in SQL and Python, deep experience with Snowflake, and skills in building ETL pipelines are essential.

Seesaw employs a variety of technologies for data engineering, primarily focusing on Snowflake for data warehousing. Additional tools may include AWS for cloud-based architectures and ETL processing technologies like AWS Glue and Airflow.

Seesaw prioritizes professional development for their Data Engineers through various stipends for continuous learning, including attendance at workshops, conferences, and access to educational resources. A culture of experimentation and innovation also encourages engineers to grow their skills.

The work culture at Seesaw is collaborative and flexible, with a strong emphasis on work-life balance. Data Engineers enjoy autonomy over their projects and are encouraged to share ideas that impact real-world users. The team actively engages in diversity, equity, and inclusion initiatives to foster a welcoming environment.

When answering this question, focus on specific projects where you've implemented Snowflake. Highlight your experience with managing large-scale deployments, how you optimized performance, and any challenges you overcame while using Snowflake features to meet business needs.

Discuss the strategies you use to monitor data quality during processing. This might include validating data against business rules, implementing automated tests, and setting up alerts for anomalies. Demonstrating a systematic approach will showcase your attention to detail.

Provide specific examples where you've encountered significant obstacles, whether they were technical, logistical, or team-related. Explain the actions you took to address these challenges and what you learned from the experience.

Emphasize your commitment to continuous learning through resources like online courses, webinars, and industry articles. Mention any communities, forums, or local meetups where you engage with other professionals to exchange knowledge.

Walk the interviewer through your process for data modeling. Discuss how you gather requirements, identify key entities, and design logical models. Highlight the importance of scalability and how you adapt models based on evolving business needs.

Highlight your specific experiences with ETL tools, mentioning the technologies you’ve worked with, like AWS Glue or Apache Airflow. Provide examples of how you’ve automated processes or improved pipeline efficiency.

Mention your emphasis on communication and aligning project goals with product and analytics needs. Describe how you facilitate discussions and feedback cycles to ensure that the data engineering work directly supports their objectives.

Reflect on qualities like problem-solving skills, ability to collaborate effectively with others, and a strong focus on user needs. Stress the importance of adaptability in a fast-paced environment, particularly in a company like Seesaw that values innovation.

Discuss your understanding of data privacy regulations, such as GDPR or CCPA, and how you incorporate compliance into your data engineering processes. Talk about measures you've implemented to ensure data security and responsible usage.

Provide a clear example of the situation, your analysis and the steps taken to troubleshoot the issue. Highlight the techniques you used to resolve the problem and any preventive measures you implemented to avoid similar issues in the future.