Backend Engineer: Experiments Team (Remote)

Constructor is the only search and product discovery platform tailor-made for enterprise ecommerce where conversions matter. Constructor's AI-first solutions make it easier for shoppers to discover products they want to buy and for ecommerce teams to deliver highly personalized experiences that drive impressive results. Optimizing specifically for ecommerce metrics like revenue, conversion rate and profit, Constructor generates consistent $10M+ lifts for some of the biggest brands in ecommerce, such as Sephora, Petco, home24, Maxeda Brands, Birkenstock and The Very Group. Constructor is a U.S. based company that was founded in 2015. For more, visit: constructor.com.

The Experiments team at Constructor builds a platform for running A/B experiments and making data-driven decisions about product changes. We also help other teams evaluate hypotheses and improve the quality of search, recommendations, and other services for our customers.

For this role, responsibilities are:

• Owning the whole experimentation process from traffic splitting to revealing experiment results. 
• Developing the internal experiments platform for ML/DS teams, implementing user-facing features for running and analyzing the results of experiments.
• Improving the performance and scalability of our services.
• Improving the trustworthiness, necessary time, and number of experiments by integrating state-of-the-art approaches for AB tests.

What we expect:

• You have hands-on experience building and owning services for production with Python.
• You have experience with monitoring and quality assurance for services that directly affect the customer experience.
• You are ready to dive into the experimental/data analytics domain
• You’re curious about how people use the product you build, not just how it looks from the technical perspective
• It will be a great match if you have experience with PySpark or ETL pipelines. This is not a Data Engineer position, but you might expect to spend 5-10% of your time improving our PySpark pipelines for calculating experiment results
• It will be a great match if you have experience building analytical services or experiment platforms.

Stack:

• Backend and user-facing services: Python, FastAPI/Flask, Plotly Dash, PostgreSQL, AWS S3
• Data pipelines: PySpark, Databricks, SQL, Airflow/Luigi
• DevOps: AWS (Cloudformation, S3, RDS), GitHub Actions, Jenkins
• Monitoring: AWS (Cloudwatch), Sentry, Amplitude