Senior Data Engineer, Fulfillment

As a Senior Data Engineer at theScore, you will be responsible for developing and maintaining APIs, orchestration systems, and data pipelines critical for data fulfillment. You'll oversee the design of data systems and enhance our data fulfillment platform. Collaboration with ML, Analytics, and Reporting teams is essential as you work to automate processes and improve overall workflows.

Candidates for the Senior Data Engineer position at theScore should have a solid foundation in computer science, with at least 5 years of experience in data engineering or software engineering. Proficiency in Python, as well as knowledge of distributed systems and relational databases, are critical. Experience with cloud platforms like AWS, GCP, or Azure is also beneficial.

A Senior Data Engineer at theScore should be comfortable working with technologies such as Kafka for messaging, Airflow for orchestrating data pipelines, and should have experience with cloud data warehouses like BigQuery or Redshift. Familiarity with Python web frameworks such as FastAPI or Flask is also advantageous.

Yes, theScore is committed to your growth and provides opportunities for professional development. As a Senior Data Engineer, you will be supported with education and conference reimbursements to ensure you stay at the forefront of your field.

The work environment at theScore is fun and relaxed, fostering collaboration among passionate individuals. The company also promotes a diverse and inclusive atmosphere, making it a great place for talented professionals to thrive.

When answering this, focus on specific projects where you designed and implemented data pipelines. Highlight tools you used, challenges you faced, and how you ensured data integrity and efficiency during the process.

Discuss strategies such as implementing rigorous testing frameworks, data validation checks, and monitoring systems to catch issues early. Provide examples of how this has impacted data integrity in your past work.

Share your methods for staying informed, such as following industry blogs, participating in online courses or webinars, and engaging with communities on platforms like GitHub or Stack Overflow.

Be specific, sharing a challenge you encountered with a data pipeline or system. Explain your approach to identifying the issue, the steps taken to resolve it, and the successful outcomes achieved.

Talk about specific cloud platforms you have used, what types of projects you completed using them, and how they impacted your workflow as a Data Engineer. Highlight familiarity with services like AWS Redshift or Google BigQuery.

Showcase your communication skills and approach to teamwork. Explain how you coordinate with teams such as Analytics and ML, ensuring alignment and fostering a collaborative environment to achieve project goals.

Detail your proficiency in languages such as Python and any compiled languages like Java or Go. Give examples of projects where you utilized these languages, emphasizing their impact on efficiency and functionality.

Discuss methods like indexing, query optimization, and effective resource allocation. Provide specific cases where you made improvements and the measurable outcomes from those optimizations.

Share your preferred tools like Apache Airflow, discussing their strengths in managing complex workflows and enabling easy scheduling, monitoring, and logging of your pipelines.

Provide details on a project involving third-party API integration, describing the process from data extraction to its usage in your system. Highlight the challenges you faced and how you ensured successful integration.