Sr Analytics Engineer

Hasbro•Boston, MA

About The Position

Hasbro is looking for a Senior Analytics Engineer to join their Enterprise Data & Engineering team. This role will focus on shaping how data is transformed, modeled, and structured to support analytics and business decision-making across the enterprise. The individual will design enterprise data models and semantic layers, build production-grade transformation frameworks and curated datasets, and ensure reporting and business metrics are accurate, consistent, and performant. Hasbro values diverse perspectives and welcomes candidates with different experiences and approaches to solving complex data challenges.

Requirements

Experience developing and delivering enterprise-scale analytics frameworks and data transformation structures in modern data environments.
Strong hands-on expertise with SQL, DBT, and Databricks, as well as cloud data platforms such as Azure and/or AWS.
Proficiency in Python or similar language for transformation logic, automation, and validation workflows.
Deep understanding of dimensional modeling, semantic layer architecture, and data warehousing standard methodologies.
Experience implementing data quality frameworks, automated testing, and documentation standards.
Ability to lead complex workstreams and influence technical decisions without direct authority.
Strong interpersonal skills, with the ability to translate data concepts into clear business-aligned structures.

Nice To Haves

Experience supporting advanced analytics or AI-enabled use cases is a plus.

Responsibilities

Design and evolution of analytics-ready data models, dimensional schemas, and curated datasets.
Own the development of transformation frameworks using DBT, Databricks, and cloud-native technologies.
Translate business requirements into scalable, reusable data models that standardize metric definitions across teams.
Establish and uphold modeling standards, semantic layer governance, naming conventions, documentation practices, and testing frameworks.
Implement and monitor data quality validation, lineage, and transformation-level controls.
Partner closely with data engineers to align upstream pipeline design with downstream modeling and consumption needs.
Collaborate with product managers, analysts, and business partners to ensure data products are intuitive, trusted, and aligned to business definitions.
Lead analytics engineering initiatives from technical design through deployment and optimization.
Recognize opportunities to simplify the analytics data landscape as well as improve performance.