Analytics Engineer

Role overview This foundational role owns the transformation layer of the data stack for a platform serving more than one million users. The position turns raw event streams, product data, AI system logs, and business data into well-structured, tested, and documented datasets that analysts, data scientists, product managers, and leadership can self-serve. The work is a bridge between data engineering and data analysis, with a heavy emphasis on data quality as a competitive advantage.

Responsibilities - Build and maintain data models, transformation pipelines, and analytics infrastructure that convert raw data into clean, reliable, and actionable datasets. - Model event streams, product data, AI system logs, and business data into structures that downstream consumers can trust. - Apply rigorous testing, documentation, and quality checks so datasets remain trustworthy across the organization. - Partner with analysts, data scientists, product managers, and leadership to translate their questions into well-shaped data. - Write performant SQL and design scalable data models that support self-service analytics.

Requirements - Strong technical depth in SQL with a track record of writing performant queries and scalable data models. - Systems-minded approach to data, with a habit of thinking in terms of data models and data quality. - Business acumen to understand the questions stakeholders need answered and shape data accordingly. - Comfort operating at the intersection of data engineering and data analysis. - Belief that clean, well-documented data is a competitive advantage.

Nice to have - Experience supporting AI improvements, product decisions, and business strategy with high-quality datasets.