Staff Data Engineer

As a Staff Data Engineer at Sovrn, you will be tasked with a variety of responsibilities that focus on enhancing our data architecture and ensuring optimal data processing. Your role includes performing architecture design, troubleshooting data processing issues, implementing big data solutions, and providing governance for business-critical data sets. You'll also focus on optimizing data access and creating data products that can be utilized by various teams within the company.

To qualify for the Staff Data Engineer position at Sovrn, you should possess a robust background in big data processing environments, specifically hands-on experience with tools such as Spark and Databricks. Additionally, familiarity with the AWS ecosystem, orchestration tools like Airflow, and proficiency in databases like Postgres and DynamoDB is required. Strong problem-solving skills and an understanding of data governance will also be essential for this role.

At Sovrn, the team dynamic is highly collaborative and cross-functional. As a Staff Data Engineer, you'll work closely with data scientists, business analysts, and product managers. Your contributions will not only be technically driven but will also focus on improving team collaboration and understanding business domains to translate them into data requirements. This synergy is critical to ensuring our data solutions are effective and meet the needs of our clients.

Data governance and security are core priorities for Sovrn. As a Staff Data Engineer, you'll work closely with the security team to implement and maintain robust data security measures. You'll also ensure compliance with important data governance and privacy regulations, such as CCPA and GDPR, which is crucial in protecting our clients' information and maintaining trust.

Sovrn is committed to fostering a culture of learning and growth. As a Staff Data Engineer, you will have the chance to stay updated with the latest big data technologies and methodologies. The environment encourages professional development, and you’ll have the opportunity to lead projects, innovate solutions, and make impactful decisions, all of which contribute to your career advancement.

When responding, focus on detailing your hands-on experience with tools like Spark or Databricks. Share specific projects where you utilized these technologies to solve complex problems or improve data management processes, illustrating your technical expertise and ability to adapt to varying data environments.

Highlight your approach to data governance. Discuss techniques such as data validation, implementing data pipeline metrics, and the importance of documentation. Provide examples from your experience where your efforts led to reliable data outputs and enhanced decision-making.

Detail the various AWS services you've used in your past roles, such as AWS S3 for storage or AWS Glue for ETL processes. Discuss specific projects where AWS played a critical role in scaling data pipelines or improving efficiency and how it aligns with the needs of a Staff Data Engineer at Sovrn.

Discuss systematic approaches to troubleshooting, such as analyzing logs, monitoring performance metrics, and collaborating with team members for insights. Provide examples of past challenges, how you identified the root cause, and the steps you took to resolve the issues effectively.

Share how you communicate technical concepts in an understandable manner to non-technical team members. Emphasize your belief in the importance of diverse perspectives in problem-solving and how you have facilitated successful projects by fostering collaboration.

Discuss your experience with orchestration tools like Airflow and how they have impacted your workflow. Highlight the significance of automating data pipeline tasks, scheduling, and managing dependencies to facilitate a seamless data processing environment.

Mention your practices for continuous learning, such as following industry publications, participating in webinars, or being involved in tech communities. This shows your commitment to professional development and adaptability to the ever-evolving data landscape.

Outline your methodologies for data modeling, including understanding business requirements and translating them into a structured format. Provide examples of models you have created and how they improved business insights and data retrieval.

Select a project that showcases your skills, detailing the problem, your approach, the technologies used, and the outcome. This narrative should emphasize how your contributions positively impacted the organization’s data capabilities.

Discuss the tools and methodologies you use for task management. Explain how you evaluate project deadlines, dependencies, and overall impact on the business to ensure you address high-priority tasks efficiently.