Lead Business Intelligence Engineer

Snap Inc.•Los Angeles, CA

2d•$162,000 - $284,000•Onsite

About The Position

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.

Requirements

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; and 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 technical teams.
Strong judgment and commitment to data quality, privacy, security, governance, and the responsible use of People data.
BS/BA degree in a technical field (e.g., computer science, mathematics) or a research-based social science field (e.g., public policy, sociology).
6+ years of relevant experience in business intelligence engineering, analytics engineering, data engineering, quantitative analysis, or a related field or a Master’s degree in a technical field + 5+ year of post-grad experience; or a PhD in a related technical field + 2+ years of post-grad experience
6+ years of experience with SQL or similar query languages and development experience in at least one object-oriented or scripting language (Python, Java, Scala, etc.); Python preferred.
3+ years of experience designing and building dashboards, reports, and other end-user analytics solutions using data visualization tools such as Looker, Tableau, Visier, Posit, or similar platforms.

Nice To Haves

MA/MS in a technical and/or research-based field.
Hands-on experience with Google Cloud Platform and data technologies such as BigQuery and Airflow.
Experience working in an agile development environment and collaborating across technical and business teams.
Experience with version control systems such as Git.
Experience working with People data, including workforce and Talent Acquisition data, and familiarity with People analytics platforms such as Visier.

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.