Staff Data Engineer

As a Staff Data Engineer at Dutchie, you will lead the design and implementation of scalable data architectures, mentor junior engineers, and build optimized ETL/ELT pipelines. Your role includes overseeing the development of data models, managing data infrastructure with tools like Snowflake, and ensuring robust data governance practices, while also focusing on innovation and strategy in data engineering.

Ideal candidates for the Staff Data Engineer role at Dutchie should have at least 8 years of experience in data engineering, with a strong background in modern data tools, database technologies, and cloud services. Expertise in Snowflake, AWS, and observability tools is essential, alongside proficiency in programming languages such as Python or Scala, making you well-prepared to tackle complex data challenges.

In the Staff Data Engineer position at Dutchie, you'll work with various modern data engineering tools and platforms, including Snowflake for data warehousing, Fivetran and Dagster for ETL processes, and AWS services for cloud-based solutions. Additionally, you will use infrastructure-as-code with Pulumi and container orchestration tools like Kubernetes to ensure efficient data workflows.

For the Staff Data Engineer position at Dutchie, essential soft skills include exceptional problem-solving abilities, strong communication skills, and the capacity to collaborate effectively with diverse teams. Leadership skills are also critical, as you will mentor junior engineers and guide them towards achieving key objectives, fostering a culture of growth and innovation.

The targeted salary for the Staff Data Engineer position at Dutchie is approximately $190,000, though this may vary based on the candidate's skills and experience. The comprehensive compensation package also includes full medical benefits, equity options, and allowances for technology resources, ensuring you are well-supported in your role.

When answering this question, highlight specific projects where you utilized Snowflake for data warehousing. Discuss how you optimized queries and managed data pipelines, focusing on the outcomes achieved and the impact on business intelligence efforts.

Demonstrate your understanding by detailing the steps you take to assess requirements, choose appropriate technologies, and establish best practices. Mention any frameworks you've used and how they've helped in maintaining performance and reliability.

Share examples of how you mentor junior engineers, focusing on techniques like pair programming or regular knowledge-sharing sessions. Discuss the importance of providing constructive feedback and fostering an environment of open communication.

Detail your familiarity with tools such as Fivetran and Dagster, and explain your process for building and optimizing ETL/ELT pipelines. Highlight any challenges you've overcome and the advantages of the solutions you implemented.

Discuss specific methodologies you apply to maintain high data quality, such as setting up automated validation tests and regular audits. Emphasize the role of documentation and team training in promoting data governance.

Outline the AWS services you're adept with, like AWS RDS and Lambda. Explain how they enhance scalability, performance, and cost-effectiveness in data engineering, sharing real-world examples of how you've leveraged these services.

Share your familiarity with observability tools like Datadog. Explain how you implement logging, alerting, and dashboards to ensure system health and performance, detailing how proactive monitoring helps in resolving issues before they escalate.

Discuss your dedication to lifelong learning by mentioning sources you use, such as industry blogs, webinars or podcasts. Highlight any recent trends you've applied in your current role, showing your proactive attitude toward innovation.

Explain how you've utilized Pulumi for automated provisioning and management in your projects. Share specific scenarios where this approach improved your deployment processes and contributed to resource management efficiency.

Share concrete examples of challenges faced in cloud environments, whether related to performance, costs, or security. Describe the approaches you took to address those challenges, emphasizing improved outcomes and learning experiences.