Data Engineer

As a Data Engineer at Motive, your primary responsibilities include building data pipelines that feed into our products for Enterprise customers. You'll also architect and design data models in collaboration with various teams, actively engage in deploying Data Ops, and work with modern tools like Airflow, AWS, and dbt to create robust data solutions that ensure the seamless flow of information.

Essential skills for the Data Engineer role at Motive include strong applied knowledge of SQL and Python, along with familiarity with data engineering tools like dbt, Airflow, and AWS. A solid understanding of data modeling, the ability to communicate effectively across multiple teams, and a knack for continuous learning and collaboration are also crucial for success in this role.

To apply for the Data Engineer position at Motive, candidates should possess a Bachelor's degree or higher in a quantitative field such as Computer Science, Mathematics, Economics, or Statistics. Additionally, a minimum of six years' experience in Data Engineering, including expertise in building modeled tables and utilizing SQL and Python, is essential.

At Motive, Data Engineers work with a cutting-edge technology stack that includes tools for data modeling and business intelligence such as dbt and Airflow, as well as cloud services like AWS. They also interact with data observability tooling and advanced data frameworks, which empower them to create high-quality, reliable data pipelines.

Collaboration is vital for the Data Engineer role at Motive. Engineers will work closely with teams across the organization, including Product and Engineering, as well as the Strategic Analytics team. Effective communication and collaboration ensure that the data pipelines and models serve the needs of the business and contribute to the overall success of our products.

In answering this question, focus on specific projects where you designed and implemented data pipelines. Discuss the tools used, the challenges faced, and how you overcame them. Mention the impact your work had on data quality and accessibility within the organization.

When discussing data modeling techniques, provide concrete examples of methodologies you have used, like star schema or snowflake schema. Reference your experience with tools such as dbt and how you ensure data integrity and efficiency in your models.

Address this question by outlining your approach to Data Ops. Discuss how you automate workflows, monitor data quality, and implement best practices to streamline operations. Include examples of specific tools you've used to ensure efficiency.

Explain your systematic approach to troubleshooting by first identifying the point of failure. Discuss any logging or monitoring tools you use and the steps you take to replicate the issue, assess the impact, and resolve it effectively.

Demonstrate your commitment to continuous learning by sharing the resources you follow, such as blogs, webinars, or online courses. Discuss any professional networks or communities you are involved with that keep you informed about the latest trends and standards in data engineering.

Talk about specific challenges, such as data inconsistency or compatibility issues between systems. Highlight how you approached these challenges and the solutions you implemented to ensure seamless data integration.

In your response, highlight your familiarity with cloud platforms like AWS. Discuss specific services you’ve used, such as S3 for storage or Redshift for warehousing, and how you’ve utilized them for scalable data solutions.

Share your method for prioritization, such as using task management tools or frameworks like Agile. Provide examples of how you adapted to shifting priorities while maintaining productivity and meeting deadlines.

Describe the QA processes you implement, including automated testing, validation checks, and monitoring ongoing data quality. Elaborate on how you address issues as they arise to maintain high standards in your work.

Discuss your openness to feedback and how you view it as a tool for growth. Highlight specific instances where you’ve received constructive criticism and how it led to improved processes or outcomes in your work.