Data Engineer

As a Data Engineer at Traba, you will architect and build the company’s scalable data infrastructure, own ETL/ELT workflows using tools like dbt and Airflow, and develop data models critical for analytics and reporting. You'll work collaboratively to ensure that the data infrastructure aligns with business objectives and adheres to industry best practices.

To qualify as a Data Engineer at Traba, you need at least 4 years of experience in Data Engineering, along with expertise in SQL and data modeling. Familiarity with modern data platforms like Databricks and Snowflake is essential, as well as experience in ETL/ELT development. Proficiency in Python for data processing is also required.

A Data Engineer at Traba will work with a variety of modern tools and technologies, including dbt for data transformations, Apache Airflow for workflow automation, and platforms like Snowflake or Databricks for data storage. Familiarity with Spark for big data processing will also be beneficial.

At Traba, we foster a growth mindset by encouraging our Data Engineers to establish best practices and provide mentorship as the team expands. We also support continuous learning by staying updated with the latest industry trends and technologies, ensuring our teams are well-equipped for the future.

Traba offers an impressive benefits package to its Data Engineers, including competitive salary, start-up equity, 100% paid health, dental, and vision coverage, flexible PTO, and various perks such as dinner provided via Grubhub, commuter benefits, and gym memberships.

In responding to this question during your Data Engineer interview at Traba, showcase specific ETL/ELT projects you’ve worked on, detailing the tools you used, challenges you faced, and how you optimized the processes for performance and reliability.

Highlight your approach to data modeling, emphasizing techniques that enhance data integrity and usability. Discuss how you consider both operational and analytical use cases, and provide examples of successful models you've built.

Discuss specific instances where you identified bottlenecks in data pipelines, the methods you employed to optimize performance, and the quantitative improvements you achieved as a result. This demonstrates your thorough understanding of performance tuning.

Talk about your understanding of data governance, including data quality assurance, lineage tracking, and observability. Illustrate with examples of how you have implemented these practices to ensure high-quality data.

Provide a detailed account of a complex data pipeline project, addressing the requirements, technologies used, the challenges you faced, and how you overcame them. This will showcase your problem-solving skills and your ability to work under pressure.

Explain how you engage with stakeholders to understand their needs, and how you incorporate feedback into your data infrastructure design. Stress the importance of iterative development and aligning data capabilities with strategic business goals.

Mention your proficiency in programming languages like Python and how you have utilized them in data processing, automation, and developing robust data pipelines. Share specific examples where applicable.

Discuss your commitment to continuous learning through industry publications, conferences, online courses, and community engagement. Sharing specific resources or institutions you follow will highlight your proactive approach.

Describe a systematic approach you would take, including initial assessments of requirements, choosing the right technologies, scalability considerations, and involving cross-functional teams for feedback throughout the process.

Discuss your specific experiences with these platforms, detailing how you used features within them for data storage, processing, or analytics tasks. Highlight how this experience would be beneficial in the Data Engineer role at Traba.