Data Engineer

As a Data Engineer at Rapsodo, your primary responsibilities will include designing, building, and maintaining scalable data pipelines for ETL processes. You'll optimize data processing performance using PostgreSQL and Google BigQuery, maintain dataflows from MongoDB to Google BigQuery, and develop dashboards in Apache Superset for effective data visualization. Additionally, you will ensure data quality and accessibility while collaborating with cross-functional teams to drive business impact.

To qualify for the Data Engineer position at Rapsodo, candidates should possess a Bachelor’s or Master’s degree in Computer Science, Data Engineering, Mathematics, Physics, or a closely related field, though equivalent practical experience is acceptable. Applicants should also have at least 2 years of experience in data engineering or similar fields, along with strong problem-solving capabilities and advanced SQL proficiency.

Essential programming skills for a Data Engineer at Rapsodo include proficiency in advanced SQL for data transformation and a working knowledge of Python for scripting and automation tasks. A basic understanding of JavaScript may also be beneficial, especially when working with BigQuery Dataform functions.

As a Data Engineer at Rapsodo, you'll work with a robust tech stack that includes PostgreSQL and Google BigQuery for database management. You will also utilize tools like Airbyte and Airflow for data pipeline automation and Apache Superset for creating visualizations and reports. Familiarity with Google Cloud Platform (GCP) will also be crucial for data processing and storage.

Rapsodo is committed to fostering professional growth for Data Engineers through extensive exposure to various non-engineering departments, providing ample opportunities to expand your skill set and experience. You'll join a newly formed data team, positioning you to take part in shaping the future of sports technology while gaining insights into business-driven data challenges.

When responding to this question, provide a detailed example of an ETL process you have designed, focusing on the specific tools and technologies used. Describe the challenges faced, how you overcame them, and the outcome of the project, showcasing your problem-solving skills.

In your response, mention specific practices such as data validation techniques, unit testing of pipelines, and securing sensitive data. Discuss any frameworks or tools you've used to monitor and audit data quality and compliance with security standards.

Share your experience with indexing, query optimization, or using database performance monitoring tools. Highlight specific situations where your strategies led to measurable improvements in performance metrics, demonstrating your analytical thinking along the way.

Provide a summary of projects or tasks involving Google BigQuery. Discuss your proficiency with its features, how you have utilized it for data analysis or reporting, and any challenges you encountered in the process, showcasing your adaptability and learning curve.

Outline your experience in working alongside non-technical stakeholders. Emphasize the importance of clear communication, understanding their needs, and how you translate technical terms into accessible language to foster collaboration and drive business solutions.

Discuss your systematic approach for identifying and resolving issues within data pipelines. Include methodologies or tools you use to trace errors, along with examples where your debugging efforts led to improved pipeline efficiency or reliability.

Share details about a project where you developed a data visualization, including the objectives, tools utilized (like Apache Superset), and how the visualizations aided decision-making. Illustrate your role in the project and the positive impact it had on the business.

Mention your methods for professional development, such as attending workshops, participating in online courses, or reading industry blogs and research papers. Highlight your enthusiasm for continuous learning and its importance in staying competitive within the field.

Discuss your perspective on current issues such as data security and privacy, handling large volumes of data, or integrating disparate data sources. Show your awareness of industry trends and how they influence the future of data engineering.

Describe your time management strategies, such as assessing the urgency and impact of tasks or utilizing agile methodologies. Provide examples of how you've successfully managed competing deadlines while ensuring high-quality work.