Analytics Engineer

What you’ll do

• Design, set up, and maintain technical data infrastructure.
• Develop production-ready, scalable data pipelines and lead the life cycle of data-related backend services.
• Create and manage data models to transform raw data into valuable assets, ensuring data accuracy and quality.
• Utilize Python and SQL for data manipulation, activation, and cleaning, organizing, and ensuring data quality for analytical purposes.
• Apply statistical analysis techniques for summarizing, interpreting, and predicting data, alongside basic machine learning concepts to explore predictive modeling techniques.

Success looks like

• Seamless technical infrastructure setup and maintenance ensures uninterrupted data flow and accessibility.
• Scalable data pipelines and optimized backend services lead to increased efficiency in data processing.
• Well-designed and maintained data models that effectively transform raw data into valuable assets, ensuring data accuracy, quality, and lineage.
• Consistently delivering accurate and reliable data models through proficient use of programming languages and statistical techniques, facilitating data-driven strategies and initiatives.
• Regularly delivering actionable insights and key metrics to commercial and product engineering teams, contributing to informed decision-making and improved business outcomes.
• Proactive problem-solving and innovative thinking in addressing data-related challenges, leading to continuous improvement and advancement in analytical capabilities.
• Exploration and application of advanced analytical techniques, such as machine learning, to uncover new opportunities and insights for the organization.

What you’ll bring with you

• At least three years of experience in software engineering or a technical analytical role.
• Ability to design and build data processing, storage, and activation systems for specific projects based on set requirements.
• Experienced in SQL for data modeling and analysis.
• Experienced in Python for building data pipelines and statistical analyses.
• Experience with dbt, GitHub, and data lakehouse solutions (ideally Snowflake).
• Able to drive software engineering best practices, such as writing maintainable and testable code.
• Efficient execution of data wrangling and cleaning processes for multiple projects.
• Ability to activate datasets and insights through reverse ETL jobs to different systems.
• Proficiency in descriptive analytics, data visualization, and basic inferential statistics.
• Independent problem-solving abilities using analytical reasoning and critical thinking skills.
• Understanding of machine learning concepts and predictive modeling techniques.