Data Engineer

As a Data Engineer at Make, you will be responsible for designing, building, and managing robust data pipelines using Airflow, ensuring smooth integration and transformation of data. You'll also develop our data platform by creating custom plugins and operators while managing cloud infrastructure primarily using AWS. Your role will involve translating complex business needs into executable data architecture, maintaining high-quality data models, and optimizing SQL data structures for various teams.

Candidates applying for the Data Engineer role at Make should have at least 3 years of experience in data engineering along with proficiency in Python for writing production-grade code. Familiarity with AWS cloud services, orchestration tools like Airflow, and infrastructure as code tools like Terraform is essential. Strong data modeling skills and understanding of data governance practices are also crucial for ensuring high-quality data solutions.

Collaboration is key for a Data Engineer at Make. You will work closely with cross-functional teams to understand their data needs, ensuring that technical solutions align with business objectives. Effective communication skills will enable you to convey complex technical concepts to non-technical stakeholders, fostering a transparent and unified workflow among varied departments.

To thrive as a Data Engineer at Make, you need strong technical skills in Python, SQL, and cloud services like AWS. Additionally, expertise in data modeling and analytics is crucial. Excellent communication skills are vital for bridging the gap between technical and business perspectives, as you will often serve as the key contact for understanding and translating data-related requirements across teams.

At Make, Data Engineers have the chance to grow professionally by leading data-driven projects, mentoring junior team members, and staying engaged in a dynamic work environment. The company supports learning through conference tickets, courses, and dedicated company learning days. There are also opportunities for leadership as you contribute to company-wide data initiatives.

In your response, highlight specific AWS services you've utilized, discussing your experience managing cloud-based infrastructure, data storage solutions, and how you've integrated these services into your data pipelines or applications.

Explain the frameworks and tools you've used for data validation and governance. Share examples of how you've implemented data quality checks and how these practices have improved the accuracy and reliability of the data.

Choose a project that highlights your technical skills and problem-solving abilities. Discuss the challenges faced, your approach to resolving those issues, and the impact your work had on the organization or team.

Provide examples of how you've successfully translated business requirements into data models. Discuss the tools and methodologies you used and how these models supported the organization’s analytics efforts.

Explain your methodology for designing scalable data pipelines. Discuss the tools you prefer, how you manage transformations, and strategies you use for operational monitoring and troubleshooting.

Emphasize the importance of collaboration. Share experiences where teamwork enhanced project outcomes and how you handle communication with both technical and non-technical team members.

Discuss the specific SQL features you've utilized to optimize data retrieval and how you've designed database structures. Mention any experience with analytics tools or data warehouses, such as Snowflake.

Share your strategies for continuous learning, such as attending workshops, engaging with online communities, and following relevant blogs or news articles that help expand your knowledge of data engineering.

Discuss your experience with tools like Terraform and how you've applied best practices in version control, modularization, and deployment strategies to manage infrastructure effectively.

Provide a clear example of a technical concept you had to explain. Describe the techniques you used to simplify the information, ensuring that the stakeholder understood its relevance and application without technical jargon.