We’re leaders in the public web data gathering industry, so our Data Team is solving loads of complex challenges with top-notch resources at hand. We sat down with Rytis Ulys, Head of Data and Analytics, to give you insights into the daily life of our Data Analysts, Analytics Engineers, and Data Engineers.
The work of Data Analysts is crucial for decision-making at our company, so they have a huge impact on the business. Since we gather, store, and process petabytes of data, our Data Analysts work with numerous data sources and solve a huge variety of different problems, which makes their job technically complex and, in turn — really interesting.
Some of their daily tasks include working together with commercial teams to deep dive into churn, retention, product performance, and A/B testing of product features. Also, they are building various automations to make the work of Oxylabs teams more efficient.
Our Data Engineers are fluent in Python and SQL and play a crucial role in designing, developing, and maintaining our Google Cloud-based data infrastructure. Leveraging tools like BigQuery, Cloud Storage, and Kubernetes on Compute Engine, they manage both stream and batch data loads from various databases, third-party applications, and APIs.
With their strong experience in data warehousing, customer data platforms, and ETL processes, they develop robust data models and implement analytics solutions to ensure seamless data flow and accessibility across Oxylabs. Thanks to their deep expertise, we have accurate, reliable, and efficient data-driven insights.
Our Analytics Engineers are strong dbt certified specialists who are keeping up with the latest technologies, testing new dbt libraries, always learning and sharing their knowledge with the rest of our teams and the dbt community.
Working with an infrastructure that handles massive amounts of data and providing solutions for large global companies comes with challenges that, I believe, are unique to Oxylabs. But our Data Engineers and Analytics Engineers have all the resources to solve them, from up-to-date tech stack to incredibly supportive teammates.
Across the teams, we mainly use Google Cloud Platform (GCS, BQ) and AWS for storage and warehousing; dbt and SQL for data modeling; Python for automation; Jupyter for ad-hoc analysis; PowerBI and Superset for data visualization; Streamlit for data applications; Airflow and Dagster for orchestration; an the latest AI models (incl. Open AI and Anthropic) for process automation.
What’s worth highlighting is that we have loads of freedom and flexibility to choose our own tools to add to the tech stack. We’re encouraging each other to explore and discover new technologies because our goal is to keep optimizing our work and staying as up-to-date as possible.
I’ve mentioned that our Data Teams get unique challenges, so here’s what helps them successfully overcome them and grow as specialists in their field.
First, you’re working alongside really strong professionals who are always happy to help you with any questions or problems. It doesn’t matter whether it’s people from your team or other departments — everyone here is down to earth, approachable, and wants to share their knowledge to help you succeed.
Then, of course, you’ll have access to vast internal and external learning resources, including community knowledge-sharing events, the Tesonet mentorship program, and much more.
One of the main things that fuels great work and professional development here is ownership. This means you get plenty of trust and freedom to deliver tasks and projects the way you think will get the best results. People here take ownership of their work while being available and helpful to their teammates — that’s how we grow, together.
If you’re passionate about Data, this is the place where you’ll find unmatched challenges and opportunities for professional development. And our team will support you every step of the way, so join us!
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