Data Engineer

As a Data Engineer at Swish Analytics, your responsibilities will include architecting low-latency analytics systems, integrating complex datasets, and building new predictive sports betting data products. You'll play a key role in developing production-level predictive analytics APIs and support our systems during live sporting events by troubleshooting any issues that arise. Additionally, your work will involve designing automated frameworks to enhance our data delivery processes.

To qualify for the Data Engineer position at Swish Analytics, you'll need a BS/BA in a related STEM field like Mathematics or Computer Science, alongside a minimum of 5 years of experience writing production-level code in Python. Proficiency in SQL, specifically MySQL, is required, along with experience in tools such as Airflow and Kubernetes. Familiarity with cloud infrastructures like AWS, web scraping, and data science concepts will also strengthen your application for this role.

Yes, the Data Engineer position at Swish Analytics is fully remote, giving you the flexibility to work from anywhere while collaborating with our dynamic team. We believe that talent knows no boundaries and are excited to welcome professionals from various locations who share our passion for sports and data.

Data Engineers at Swish Analytics predominantly work with Python and SQL, leveraging tools like Airflow for workflow management and Kubernetes for container orchestration. You will also have the opportunity to work with AWS for cloud services, REST APIs for data integration, and various technologies for building end-to-end ETL pipelines.

As a Data Engineer at Swish Analytics, you can expect a competitive salary ranging from $120,000 to $165,500, depending on your experience and qualifications. We value the hard work and expertise of our team members, ensuring that our compensation reflects the talent that drives our innovative projects.

When answering this question, highlight specific projects where you've utilized Python and SQL to solve real-world problems. Explain your role, the tools you used, and how your contributions benefited your team or organization, showcasing your technical strengths.

Share an example of an ETL pipeline you have built, discussing the tools you used (like Airflow or custom scripts) and the complexities you faced. Emphasize your ability to troubleshoot and optimize these pipelines, ensuring data integrity and performance.

Discuss your systematic approach to troubleshooting issues, such as monitoring metrics, using logging tools, and creating contingency plans. Explain how you prioritize issues and collaborate with your team to resolve problems efficiently during critical events.

Provide examples from your past roles where you've successfully integrated real-time data into applications or systems. Discuss the technologies utilized, challenges faced, and the impact of your work on business outcomes, showcasing your expertise in this area.

This is your chance to speak about your hands-on experience with AWS services. Share specific instances where you deployed applications, managed databases, or utilized cloud services to enhance the performance and scalability of your projects.

Express your passion for sports and any personal experience with betting. Discuss how your understanding impacts your approach to data analysis and system design at Swish Analytics, emphasizing your drive to create solutions that resonate with users.

Choose an example of a challenging project that involved your technical skills and teamwork. Explain the hurdles you faced, the solutions you implemented, and what you learned from that experience, demonstrating resilience and adaptability.

Convey your curiosity and commitment to professional development. Mention specific resources such as blogs, conferences, online courses, or communities that you actively engage with to grow your knowledge and stay informed about the latest tools and technologies in data engineering.

Detail your familiarity with Git, focusing on how you've used it in collaborative projects. Discuss practices such as branching, committing changes, and resolving merge conflicts, illustrating your understanding of version control and its importance in software development.

Highlight your coding standards, such as writing clean, well-documented code, conducting code reviews, and testing before deployment. Share how these practices contribute to higher code quality and system reliability, stressing your commitment to excellence in your work.