Senior Software Engineer - Data

As a Senior Software Engineer - Data at Vendelux, your responsibilities will include developing and managing high-quality data pipelines, integrating machine learning models, and ensuring the effectiveness of our data infrastructure. You'll work closely with data scientists and other teams to enhance data processing speed and reliability, contributing to the improvement of our AI-powered platform.

To be considered for the Senior Software Engineer - Data role at Vendelux, candidates should have a minimum of 6 years of relevant engineering experience, particularly with data and data pipelines. An expert level in Python, a proven track record in delivering impactful features, and strong communication skills are essential. Previous startup experience is advantageous but not a requirement.

Vendelux embraces a remote-first culture, which means as a Senior Software Engineer - Data, you will have the flexibility to work from anywhere. Our collaborative workstyles emphasize communication through chats and shared documents, allowing you to effectively engage with your colleagues and contribute to a vibrant engineering team.

While previous startup experience can be beneficial, it isn't mandatory for the Senior Software Engineer - Data role at Vendelux. What matters most is your passion for technology, data, and innovation, plus your readiness to adapt and make an impact in a dynamic environment.

At Vendelux, as a Senior Software Engineer - Data, you will primarily work with Python, Dagster, MySQL, and Snowflake. Since we’re in the early stages, we encourage exploring changes to our tech stack to enhance performance and adaptability in line with our company’s goals.

At Vendelux, there is significant potential for growth as a Senior Software Engineer - Data. You will have the chance to take ownership of data processes, mentor junior engineers, and be part of critical decision-making around infrastructure improvements, paving the way for your professional development in a fast-growing company.

Vendelux fosters a culture of collaboration and learning. As a Senior Software Engineer - Data, you can expect an environment that promotes knowledge-sharing, mentorship, and open communication across engineering and product teams. Our focus is on building relationships and continuously improving our engineering practices.

When discussing your experience with data pipeline development, highlight specific projects where you built or improved data pipelines. Discuss the technologies you used, the challenges you faced, and how you optimized the process for efficiency and reliability.

For this question, explain the steps you take to ensure a seamless integration of machine learning models into data workflows. Discuss how you collaborate with data scientists, the technologies you leverage, and how you measure the effectiveness of the integration.

When answering this question, elaborate on the strategies you implement for maintaining data quality and integrity. Mention your experience with data validation, anomaly detection, and how you utilize monitoring tools to ensure continued data accuracy.

Be prepared to talk about your proficiency with data tools such as Snowflake or Dagster. Discuss how you've used these tools in your previous roles, any advanced features you’ve implemented, and the impact they had on your data processing tasks.

Explain the challenges you've encountered when building scalable data infrastructures, detailing both technical and organizational obstacles. Focus on how you successfully navigated these challenges and the solutions you implemented to overcome them.

This is an opportunity to showcase your Python skills. Discuss your experience with Python, including how long you've used it, projects you've completed, and specific libraries or frameworks you're proficient in. Provide examples of complications you solved using Python.

Highlight the importance of collaboration in engineering by citing examples from your past work. Discuss how collaboration improves problem-solving, encourages knowledge sharing, and fosters a culture of innovation within an engineering team.

Mention how you approach task prioritization by assessing the impact and urgency of tasks. Discuss tools or methodologies you use for task management and how you communicate with team members to maintain clarity and focus.

Discuss critical aspects of successful ETL processes such as extraction efficiency, transformation accuracy, and loading speeds. Emphasize on continuous monitoring and iteration to improve ETL workflows and how you’ve applied these principles in your work.

When addressing technical debt, explain how you identify, assess the impact of, and prioritize addressing it within a project. Share examples of decisions you made that balanced immediate needs with long-term maintenance and scalability.