Senior Data Engineer

As a Senior Data Engineer at Synthesia, you will own data pipelines from ingestion to transformation, ensuring scalable and observable data infrastructure for the Applied Research team. Your responsibilities will include designing high-throughput systems for efficient data access, re-architecting ingestion pipelines, implementing data tiering and modeling patterns, and supporting researchers with quick data retrieval processes.

To qualify for the Senior Data Engineer position at Synthesia, we look for candidates with a strong background in data engineering, including experience with data pipeline ownership, SQL, Python, and data architecture patterns like lambda or kappa. Familiarity with orchestration tools such as Kubeflow and Spark, as well as hands-on experience with columnar formats like Delta Lake, is essential for success in this role.

At Synthesia, we prioritize career growth for our team members. As a Senior Data Engineer, you will have ample opportunities to enhance your skills through hands-on projects, mentorship, and a culture that encourages continuous learning. You'll also be part of a dynamic team that fosters collaboration and innovation, helping you advance in your career while working on impactful projects within a fast-paced environment.

The Senior Data Engineer role at Synthesia is unique because it sits at the convergence of research, infrastructure, and product, rather than following a traditional engineering path. You will engage deeply with high-impact projects while having the autonomy to create data systems based on real-world use cases, ensuring you contribute meaningfully to the company's vision of making video accessible for all.

Synthesia boasts a vibrant company culture that emphasizes collaboration, creativity, and a strong sense of community. Employees enjoy a hybrid work environment with options for remote work, and there are regular planning sessions and social events to foster team bonding. With a focus on employee well-being, professional development, and a strong referral scheme, Synthesia creates an engaging workspace for its team members.

When answering this question, detail specific examples of data pipeline projects you have led, emphasizing your role in their end-to-end development, including aspects like data ingestion, transformation, and modeling. Show how you solved challenges and ensured data quality and integrity throughout.

Share your experience with various data orchestration tools and frameworks, especially those relevant to Synthesia's stack like Kubeflow and Spark. Explain why you prefer these tools and how they've improved efficiency and scalability in your previous roles.

Use this opportunity to illustrate your problem-solving skills. Discuss a complex issue involving data quality or pipeline inefficiencies that you tackled through innovative solutions. Highlight your analytical approach and how you collaborated with others to overcome obstacles.

Elaborate on your methods for maintaining data quality, such as implementing automated testing, monitoring systems, and observability practices. Mention specific metrics you track to ensure pipelines function optimally.

Discuss your design philosophy by emphasizing patterns you've employed, such as dimensional or medallion modeling. Share how your decisions were influenced by specific use cases and business needs, and how it aligns with Synthesia’s focus on scalability.

Detail your experience with cloud platforms and how you've leveraged them for scalability and cost management. Provide examples of specific projects where cloud services significantly improved data management and team collaboration.

Talk about your familiarity with MLOps practices and how you've integrated machine learning aspects into your data pipelines. Explain any tools or frameworks you've used and the impact on the research or business outcomes.

Explain your prioritization framework, whether it's based on project impact, deadlines, or team needs. Share an example where prioritization helped deliver on important goals under tight timelines.

Share your opinion based on your experiences with both processes and cite specific scenarios where one approach was more beneficial than the other. This will showcase your understanding of the nuances within data engineering methodologies.

Customize your answer to reflect Synthesia's mission of making video accessible and easy to create. Discuss what excites you about the company's innovative culture, its rapid growth, and the opportunity to work on high-impact data projects.