Backend Engineer: Experiments Team (Remote)

As a Backend Engineer on the Experiments Team at Constructor, your primary responsibilities include owning the entire experimentation process from traffic splitting to analyzing and presenting experiment results. You'll develop the internal experiments platform for our machine learning and data science teams while continuously improving the performance and scalability of our services.

To be considered for the Backend Engineer role at Constructor, you should possess hands-on experience building and managing production services using Python. Familiarity with monitoring and quality assurance processes is essential, as well as an eagerness to delve into experimental and data analytics domains. Experience with PySpark or building analytical services is also highly desirable.

Constructor is dedicated to the professional growth and development of its employees. As a Backend Engineer, you'll benefit from a training and development budget renewed yearly, providing opportunities to enhance your skills and advance your career. You’ll also work alongside smart colleagues who will support your growth while making a meaningful impact in ecommerce.

Absolutely! Constructor offers a fully remote work environment, allowing you to choose where you live and work. You'll enjoy the flexibility of remote work while being part of a dynamic and inclusive team focused on transforming ecommerce.

Working as a Backend Engineer at Constructor comes with a range of appealing benefits, including unlimited vacation time, a provided Apple laptop, a work-from-home stipend for setting up your home office, and stock options. Additionally, you’ll have access to a supportive work environment that emphasizes diversity and inclusion.

When taking ownership of the experimentation process, I focus on clearly defining the experiment's objectives, selecting appropriate metrics for evaluation, and ensuring accurate traffic splitting. After implementing the experiment, I monitor its progress closely, analyze results, and derive meaningful conclusions to inform product decisions.

In my previous roles, I have utilized Python extensively for building scalable backend services. I approach development with an emphasis on clean code and efficient algorithms. Python's versatility allows me to implement complex features and maintain robust applications while adhering to best development practices.

To enhance service performance and scalability, I focus on optimizing database queries, leveraging caching techniques, and using efficient data structures. I conduct load testing to identify bottlenecks and work on code optimizations that can significantly improve responsiveness and resource utilization.

User feedback is vital for me; I actively seek out and analyze it to gain insights into user experiences. After collecting feedback, I prioritize changes based on user needs and implement modifications to the product iteratively, ensuring each update enhances user satisfaction and engagement.

I have conducted numerous A/B tests to validate hypotheses and drive product improvements. I focus on defining clear goals for experiments, setting up proper tracking, and ensuring a significant sample size. After executing the tests, I analyze results using statistical methods, which help determine the optimal direction for future iterations.

For monitoring and quality assurance, I utilize tools such as AWS Cloudwatch for system performance metrics and Sentry for error tracking. Additionally, I implement automated tests and continuous integration processes to ensure that new changes don’t introduce issues, maintaining system reliability and customer trust.

I believe clear communication and alignment are key to overcoming challenges in cross-functional collaboration. I ensure all stakeholders understand the project goals, timelines, and roles. Regular check-ins help address concerns early and maintain momentum between teams, ensuring project success.

While this position is not primarily focused on data engineering, I have experience with data pipelines and ETL processes. I understand how to manage data flow and transformation using tools like PySpark and SQL, which enables me to analyze experimental results efficiently and contribute to improving analytics workflows.

In a previous role, I was tasked with scaling an underperforming service. After analyzing the architecture, I identified key bottlenecks and restructured the codebase for better performance. By implementing load balancing and optimizing database interactions, I was able to significantly enhance service responsiveness and reliability.

When managing multiple projects, I prioritize tasks based on deadlines, impact, and dependencies. I use project management tools to visualize tasks and ensure transparency among team members, making it easier to adjust priorities when necessary and stay focused on delivering value effectively.