The Role
Simon & Schuster is investing in Data & Decision Intelligence to build a decision ecosystem in which data, tools, and data-driven automation are embedded in how work gets done every day - enabling faster, more informed decisions across the company.
We are seeking a Senior Analytics Engineer to help turn that commitment into trusted, reusable data products across publishing, sales, marketing, finance, and operations. This senior hands-on role combines analytics engineering, business partnership, and responsible use of AI to deliver governed data models, consistent metrics, and scalable self-service analytics.
What You Will Do
Build trusted data products. Develop scalable transformation pipelines, dimensional models, curated data marts, and reusable datasets for reporting, forecasting, experimentation and AI-enabled use cases.
Create consistent metrics. Partner with business leaders to define critical measures and encode consistent business logic so teams work from shared definitions across dashboards, analyses, applications, and AI experiences.
Engineer for trust. Apply automated testing, documentation, lineage, monitoring, code review, and data-quality controls; resolve issues at their root cause.
Enable responsible AI. Use approved AI tools to accelerate delivery, prepare governed data for AI applications, and validate generated outputs for accuracy, explainability and business fit.
Automate manual processes. Partner with business and operations teams to replace repetitive, data-intensive workflows with reliable, monitored automation that improves speed, accuracy, control, and employee capacity.
Modernize analytics. Transition fragmented or legacy reporting into governed data products and self-service experiences while maintaining business continuity and controlling platform cost.
Lead through influence. Translate ambiguous questions into practical solutions, communicate recommendations clearly, mentor colleagues, and raise team engineering standards.
Partner across the enterprise. Work with stakeholders in publishing, sales, marketing, finance, operations, supply chain, and technology, including globally distributed colleagues.
Drive adoption. Document solutions, explain data products in business language, and coach users to confidently interpret and apply data
What You Bring
5+ years in analytics engineering, data engineering, BI engineering, or a related data role.
Advanced SQL, dimensional modeling, and experience with structured and semi-structured data.
Hands-on experience with a modern cloud data platform and transformation framework, such as Snowflake and dbt or comparable technologies.
Practical Python skills and experience with Git, modular design, testing, code review, CI/CD, and production support.
Experience defining governed business metrics and delivering analytics through Power BI, Tableau, Looker, or a comparable platform.
Strong business partnership, communication, and problem-solving, with sound judgment around sensitive data and AI-assisted tools.
Experience mentoring colleagues and improving team practices through feedback and shared standards.
Preferred
Experience with semantic layers, observability, metadata, lineage, or data contracts.
Experience with commercial, supply chain, and marketing analytics
Experience modernizing legacy reporting and enabling governed self-service analytics.
Familiarity with generative AI, or AI-product evaluation.
Publishing, media, retail, or consumer-products experience
Bachelor's degree in a quantitative or technical field, or equivalent practical experience.
Will Be Measured
Business teams trust and adopt shared data products and metric definitions.
Critical pipelines and models are reliable, observable, documented, and cost-efficient.
Decision cycles become faster, with less manual reconciliation and duplicate reporting logic.
Manual processes are reduced through reliable automation with measurable improvements in cycle time, accuracy, controls, and employee…