Senior Data Engineer

As a Senior Data Engineer at Zego, your primary responsibilities will include developing and maintaining ETL and ELT pipelines, supporting data scientists with machine learning models, evolving our data architecture, and collaborating across teams to launch new products. You'll play a vital role in driving the growth of our data platform, focusing on strong data modeling, quality, and efficiency.

Ideal candidates for the Senior Data Engineer role at Zego should possess skills in Python and SQL, have a solid understanding of data pipelines and infrastructure, and experience with databases such as PostgreSQL, Redshift, or Snowflake. Familiarity with tools like Airflow, Terraform, and a passion for continuous learning are also essential.

Zego is committed to fostering career growth for its Senior Data Engineers by providing opportunities for learning, mentorship, and collaboration with cross-functional teams. We embrace the latest technologies and encourage our engineers to experiment and share their insights, contributing to both personal and professional development.

In the role of Senior Data Engineer at Zego, you'll work with an exciting tech stack that includes Python, SQL, Airflow, Data Build Tool (DBT), PostgreSQL, and various AWS services. You will also explore infrastructure as code tools like Terraform and concepts such as Data Lakes and Data Meshes.

Zego fosters a unique hybrid work culture, allowing Senior Data Engineers to balance collaboration with flexible home-working. We emphasize teamwork and productivity, enabling our employees to choose how they meet their work commitments while spending at least one day a week in our central London office.

As a Senior Data Engineer at Zego, you will receive a competitive salary, private medical insurance, company share options, generous holiday allowances, and a range of wellbeing benefits. We also support diversity and inclusion, providing an equitable environment for all employees.

To apply for the Senior Data Engineer position at Zego, you can submit your CV along with a customized cover letter detailing your relevant experience and why you are excited about the opportunity to join our innovative team. We review applications carefully and look forward to hearing from you!

When answering this question, detail specific projects where you utilized ETL or ELT processes, including the tools and technologies used. Highlight your understanding of data transformation and loading strategies, and how they improved the overall data pipeline efficiency.

Discuss the various techniques you've implemented to monitor and validate data quality, such as automated tests, validation checks, and error handling strategies. Emphasize the importance of data consistency and reliability in your data engineering work.

Share your hands-on experience with data warehousing solutions like Redshift or Snowflake, and elaborate on how you've applied specific best practices for data architecture and dimensional modeling to facilitate efficient querying and reporting.

Provide examples of how you've leveraged various AWS services in data engineering projects, such as S3 for storage, RDS for databases, or Lambda for serverless computing. Explain how these services have contributed to the scalability and reliability of your solutions.

Illustrate your commitment to continuous learning by mentioning specific industry blogs, conferences, or courses you follow. Talk about interesting trends or technologies you're excited to explore further and how you plan to apply them in your work.

When discussing a challenging data project, outline the problem, your approach to analyzing it, and the actions you took to resolve it. Highlight collaboration with team members, innovative solutions, and the positive impact it had on the organization.

Explain your methodologies for efficiently handling large datasets, such as data partitioning, indexing, or employing distributed computing technologies. Detail any specific tools or libraries you use to enhance data processing speeds.

Share your insights on data architecture approaches like Data Lakes or Data Meshes. Discuss how you would integrate diverse data sources, ensure accessibility for data consumers, and maintain data quality and compliance throughout the implementation.

Emphasize the importance of collaboration with cross-functional teams like data scientists and product managers. Describe specific instances where teamwork was vital to a project's success, demonstrating your ability to communicate effectively and share knowledge.

Illustrate your organizational skills by discussing how you assess project timelines, deadlines, and dependencies. Share specific strategies you employ for prioritizing tasks to maintain productivity and ensure timely project delivery.