At Going, a Senior Data Engineer plays a crucial role in designing and building robust data infrastructures that support high-impact decision-making across various teams. This involves architecting complex data pipelines, optimizing ETL processes, and implementing data governance practices. The goal is to ensure that our data systems are scalable, efficient, and capable of delivering exceptional value to our members.

The Senior Data Engineer role at Going requires over six years of experience in data engineering, with at least two years in a technical leadership capacity. Key skills include proficiency in big data technologies like Spark and Kafka, experience with cloud platforms such as AWS and Databricks, and expertise in Python and SQL. Knowledge of containerization tools like Docker is also beneficial.

Going fosters a supportive and inclusive work culture that values collaboration and authentic self-expression. As a Senior Data Engineer, you will be part of a team that celebrates diversity and is committed to creating a positive work environment. The company also encourages continuous personal and professional development through reimbursement for learning and growth opportunities.

A Senior Data Engineer at Going plays a vital role in promoting and enhancing the company's data-driven culture. By designing and maintaining efficient data pipelines and collaborating with data science teams, you'll enable advanced analytics and machine learning projects that drive decision-making and improve user experiences. Your leadership will help shape and elevate the overall data strategy for the company.

Absolutely! At Going, we believe in nurturing talent and providing opportunities for growth. As a Senior Data Engineer, impressive performance and leadership can lead to further advancement within the data engineering team or even into broader technical or managerial roles in the company. Your career trajectory can be directly influenced by the initiatives and projects you undertake.

As a Senior Data Engineer at Going, you can expect to work on exciting and complex projects, such as designing data lakehouses, optimizing ETL processes, and improving data quality across multiple cloud platforms. You'll also collaborate with various teams to enhance machine learning models and support advanced analytics—having a direct impact on how we serve our travel community.

Going offers a competitive benefits package for Senior Data Engineers that includes a remote work environment, comprehensive health insurance, a 401(k) plan with matching contributions, and a generous open vacation policy. Additionally, employees receive stipends for wellness and professional development, along with opportunities for travel and team retreats.

When discussing your experience with big data technologies, focus on specific tools you've used, like Spark or Kafka. Explain how you've implemented these technologies in previous projects, detailing challenges you faced and how you overcame them. Highlight your role in optimizing data pipelines and ensuring high availability within large-scale systems.

To approach designing data pipelines and ETL processes effectively, emphasize your methodical planning and implementation stages. Discuss how you assess data sources and requirements, select appropriate architecture, and ensure scalability and efficiency. Including real-life examples of successful data pipeline projects can solidify your explanation.

Share your experiences with cloud platforms such as AWS EMR or Glue, focusing on how you've utilized these services to manage and analyze data. Highlight specific projects that utilized cloud technologies, detailing performance optimizations, cost-effectiveness, and enhanced data management that led to successful outcomes.

Explain your strategies for ensuring data quality and governance, such as implementing validation checks and data profiling metrics. Discuss frameworks or certifications that back your practices, and how you collaborate with other teams to maintain consistent data governance across platforms and processes.

When discussing cross-functional team experiences, share specific instances where you collaborated with data science, analytics, or product teams. Describe your role in translating technical jargon into actionable insights for non-technical stakeholders, and how this collaboration improved overall project success.

Mention common tools that facilitate best practices in data engineering, such as dbt and Airflow for managing workflows and data transformations. Discuss how you apply these tools to enhance process efficiency and maintain high-quality standards in data engineering tasks within your projects.

In your response, you could emphasize time management techniques such as Agile methodologies or prioritization frameworks (like MoSCoW). Share specific examples of how you've successfully balanced workload across various tasks while ensuring project deadlines were consistently met.

Describing a challenging issue could involve providing a detailed narrative of the challenge, your thought process in troubleshooting, and the resolution steps taken. Highlight the lessons learned and how those experiences shaped your approach to similar problems in future projects.

Share the resources or methods you employ to stay up to date, including attending conferences, participating in online courses, or following industry publications and influencers. Discuss how your proactive approach to learning helps you bring innovative ideas and technologies to your role as a Senior Data Engineer.

Mentoring can be approached by discussing the importance of fostering a collaborative environment where junior engineers feel supported. Describe specific strategies, like regular check-ins, knowledge sharing sessions, and providing hands-on training, to illustrate how you guide and develop their technical skills and confidence.