Staff IT Data Engineer

As a Staff IT Data Engineer at Databricks, your main responsibilities include owning the data strategy and design, developing data products that address various business use-cases, and collaborating closely with teams across the organization. You will lead technical discussions, guide best practices in data warehousing, and analyze business requirements to inform your work. The goal is to create simple and elegant data solutions that meet company objectives and enhance data intelligence.

To qualify for the Staff IT Data Engineer position at Databricks, you should have at least 8 years of related experience, which can be complemented with a Bachelor’s degree in fields like Computer Science or Statistics. Alternatively, equivalent experience can be considered. We are also looking for hands-on expertise in big data architectures, relational databases, and programming languages such as SQL and Python. Previous experience in building data pipelines from business applications is highly beneficial.

Essential skills for the Staff IT Data Engineer at Databricks include extensive experience with data modeling and architecture design, knowledge of message queuing, stream processing, and big data stores, along with advanced SQL and Python skills. Familiarity with tools like Databricks and BI tools such as Tableau or Looker is a plus. Strong analytical abilities and experience in collaborating with cross-functional teams are also critical to success in this role.

Collaboration is key for a Staff IT Data Engineer at Databricks. You will work closely with various stakeholders, including analysts and data scientists, to identify their data needs and ensure that data products are accurately designed and delivered. This role involves frequent communication and the ability to translate complex technical concepts into simple solutions that can be easily understood and implemented by diverse teams.

As a Staff IT Data Engineer at Databricks, you will have a profound impact on the company's ability to leverage data for decision-making and strategy formulation. Your work will directly influence data-driven business outcomes, enhance efficiency through optimized data pipelines, and contribute to the overall growth and direction of the company’s innovative data and AI initiatives.

In addressing this question, discuss specific projects where you've designed data schemas focusing on performance optimization. Mention your approaches to modeling for both read and write operations, and provide examples of how these designs improved efficiency in real-world applications.

When responding, highlight your methodical approach to pipeline construction. Talk about the tools and technologies you've used, challenges you've faced, and how you ensured data integrity and efficiency throughout the pipeline’s lifecycle. Providing numbers and metrics that demonstrate improved performance can create a strong narrative.

Focus on your analytical process when gathering business requirements. Explain how you engage with stakeholders to understand their needs, document requirements thoroughly, and translate them into technical designs. Providing examples of successful projects will bolster your answer and demonstrate your effectiveness in this area.

Share a specific instance where you encountered cross-functional team challenges, detail how you navigated communication barriers, and what strategies you employed to ensure collaboration. Highlighting positive outcomes and lessons learned can showcase your ability to work well in diverse teams.

When discussing this, convey a thorough understanding of root cause methodology. Provide a concrete example of data inconsistencies, how you identified the problem, the steps taken to resolve it, and how you implemented long-term solutions to prevent recurrence.

Illustrate your commitment to continuous learning. Mention professional development activities such as attending industry conferences, participating in online courses, or how you engage with the tech community through forums or blogs. Showing enthusiasm for trends like AI/ML can also align with Databricks' innovative culture.

Talk about your specific experiences with cloud platforms like AWS, Azure, or Google Cloud, focusing on how you leveraged their services for data storage and processing. Highlight projects where cloud solutions improved efficiency, scalability, or reduced costs.

Detail your comprehensive approach to maintaining data quality through validation and cleansing processes. Explain the methods you employ for monitoring data integrity and how you utilize metrics to ensure ongoing compliance with quality standards.

Discuss any AI/ML projects you've worked on, focusing on your role in data preparation, feature engineering, or model deployment. Highlight how strong data engineering practices positively impacted the success of these projects.

Share your perspective on the complexities involved in balancing technical requirements with business needs. Talk about how adapting to rapid changes in technology while ensuring collaboration and maintaining quality can be challenging, and share strategies you've developed to handle these pressures.