As a Senior Analytics Engineer at bsport, you will play a key role in designing and optimizing data analytics solutions to support our fast-growing business. You will be responsible for building and maintaining robust data pipelines, enabling teams to make well-informed decisions based on accurate and relevant data. This position offers the opportunity to work on diverse strategic projects, spanning product analytics, marketing, sales, and customer service.

Your main responsibilities:

• Design and develop data pipelines in Python and SQL to automate large-scale data analysis.

• Collaborate closely with stakeholders across product, marketing, sales, and customer service teams to define data needs and design tailored solutions, directly impacting business decisions.

• Ensure data quality by implementing ETL processes and maintaining the integrity of data flows.

• Provide valuable insights through data analysis, influencing strategic decisions with both technical and business responsibilities.

• Contribute to the evolution of data infrastructure, ensuring scalability as the company continues to grow.

• Work with Databricks to process large datasets and orchestrate workflows efficiently.

We are looking for an Analytics Engineer, but we are also open to applications from Data Engineers with strong analytical skills or Data Analysts with a solid technical background. You should be autonomous, capable of transforming complex data into actionable insights, and comfortable working in a fast-paced, ever-evolving environment.

Required Skills:

• 4+ years of experience as an Analytics Engineer, Data Engineer with an analytical mindset, or Data Analyst with strong technical skills.

• Expertise in Python and SQL for developing data pipelines and managing datasets.

• Experience in ETL and data flow management.

• Familiarity with Databricks is a plus, as the role involves processing and orchestrating large-scale data workflows.

• Ability to work closely with cross-functional teams and design solutions tailored to business needs.

• Experience in a scale-up or dynamic environment is a plus.

• 30 min with the Lead Data – Discussion about the role’s requirements and your fit for the position.

• 15 min with Talent Acquisition – Administrative discussion and review of your background.

• 1h30 technical interview – In-depth session with a Data team member to assess your expertise in Python, SQL, and the technical challenges from your past projects.

• 1h with the Lead Data – Conversation about the impact of your previous projects, role expectations, and responsibilities.

• 1h with the CPO – Final discussion.