Lead Business Intelligence Engineer

About the role

We are looking for a Business Intelligence Engineer for Snap’s People Team. This person will be a trusted business partner who brings together technical expertise, business acumen, and analytical rigor. They will work closely with engineering, People Science, business leaders, and HR leadership to lead business intelligence and analytics engineering initiatives, design scalable data solutions, deliver meaningful insights, and enable data-informed decision-making.

Responsibilities

• Lead complex, high-impact business intelligence and analytics engineering initiatives from problem definition through delivery.
• Translate ambiguous business needs into clear technical and analytical approaches, working with stakeholders to define requirements, evaluate tradeoffs, and prioritize solutions.
• Independently own your workflow and delivery, using sound judgment to prioritize work, manage timelines and dependencies, address risks or blockers, and keep stakeholders informed.
• Design and architect reliable, scalable data pipelines, data models, and self-service analytics solutions using tools such as SQL, Python, BigQuery, Airflow, Visier, Looker, and Posit.
• Own key data products or systems end-to-end, including technical design and architecture, quality, reliability, documentation, monitoring, maintenance, and continuous improvement.
• Conduct rigorous analysis and translate complex findings into clear insights and recommendations for technical and non-technical audiences.
• Identify opportunities to simplify processes, improve data quality, reduce manual work, and build more scalable solutions.
• Provide technical guidance and mentorship to team members, contribute to team standards and best practices, and help foster a culture of learning and high-quality execution.

Skills

• Advanced SQL and Python skills, with experience designing and building reliable data pipelines, models, and analytics solutions, and the ability to leverage AI-assisted development and analytics tools responsibly; willingness to adopt new technologies as needs evolve.
• Deep understanding of the data ecosystem, including data warehouses, dimensional and semantic modeling, ETL/ELT pipelines, analytics layers, visualization tools, and self-service data products.
• Demonstrated ability to design reliable, scalable data architectures and improve data quality, performance, maintainability, and operational resilience.
• Ownership and accountability, with the ability to independently navigate complex or ambiguous problems, make sound technical and architectural decisions, manage priorities across multiple workstreams, and drive work forward with limited direction.
• Experience applying analytical and statistical methods, including descriptive analysis, regression, correlation, time series analysis, and hypothesis testing, using tools such as Python or R.
• Business acumen and experience translating business questions into clear metrics, analytical requirements, data solutions, and actionable insights, with careful attention to scope, consistency, and interpretation.
• Ability to lead through influence and communicate complex technical and analytical concepts clearly across executives, business partners, analysts, and