Data Engineer

As a Data Engineer at Goldbelly, you'll take on a variety of responsibilities including collaborating with engineers and analysts to design optimized data pipelines, maintaining scalable data systems for high availability, and improving our data infrastructure. You'll ensure that data quality and governance are upheld across workflows, and actively build data models that support business insights.

Candidates for the Data Engineer position at Goldbelly must possess a minimum of 3 years of experience in data engineering, have expert-level SQL skills, and preferably know Python. Additional qualifications include expertise in data modeling, familiarity with cloud data infrastructure such as AWS and Snowflake, and experience with data ingestion tools.

Collaboration plays a vital role for a Data Engineer at Goldbelly. You'll be working closely with full stack engineers, machine learning engineers, data analysts, and product managers to ensure that data pipelines meet business needs and drive valuable insights. Being a team player is key!

At Goldbelly, Data Engineers are expected to work with a range of tools and technologies including dbt Cloud for data transformation, Fivetran for ETL pipelines, cloud services like AWS, and BI platforms such as Metabase for data visualization. Experience with Git for version control is also essential.

The salary range for the Data Engineer position at Goldbelly is between $140,000 and $190,000, depending on your experience and performance in the interview. Additionally, employees receive equity options, benefits, and the opportunity to work within a passionate team.

When answering this question, focus on specific projects where you utilized SQL to handle large datasets. Discuss how you wrote complex queries for data extraction and manipulation and emphasize your proficiency in various SQL functions.

In your response, discuss strategies like regular data validation checks, implementing data governance practices, and setting up monitoring systems to catch anomalies. Mention how you've successfully improved data quality in past experiences.

To answer this, discuss methods such as reducing data redundancy, implementing efficient ETL processes, and leveraging tools to streamline data ingestion and storage. Provide an example of a pipeline you optimized and the results achieved.

Discuss your familiarity with cloud services, especially AWS or Snowflake, and how you've leveraged cloud resources for storage and processing. Mention specific projects where you used these platforms effectively.

Here, highlight any projects where you implemented ETL solutions using Fivetran. Discuss the nuances of setting up data integration and how you ensured the output met stakeholder expectations.

Respond by highlighting your communication skills and providing examples of successful collaboration with engineers, analysts, and product managers. Emphasize your approach to understanding their data needs.

In response, discuss any experience you have with event-driven architectures and tools like Confluent Kafka. Provide examples of how real-time data streaming positively impacted a project or business decision.

Share your methodology for creating data models that support analytical needs. Discuss the importance of considering the end-users when designing models and how you've adapted to various business requirements.

Explain your approach to professional development, such as attending workshops, following industry leaders on social media, and participating in webinars, ensuring you're updated on the latest data engineering advancements.

Answer by detailing your experience with version control systems like Git. Discuss how important version control is in maintaining the integrity of your code and collaboration within teams, providing specific examples of your practices.