Data Platform Engineer Boston, Massachusetts

Job Overview

Wetake play seriously. We’re looking for curious adventurers ready to find their party, fueled by imagination and drive to build what’s never been built before. At Hasbro and Wizards of the Coast, you’ll collaborate with passionate teams to reimagine our iconic brands and create experiences that spark joy, connection, and community through the magic of play. This is your chance to shape legendary play that lasts a lifetime.

We’re looking for a Data Platform Engineer to join the team responsible for the infrastructure, frameworks, and tools that power data and analytics across the company. In this role, you'll help build and maintain the shared platform that data engineers, analytics engineers, data scientists, and ML engineers depend on every day. You'll work closely with senior platform engineers, learning how to build and operate large‑scale data infrastructure while taking on real ownership of platform components as you grow.

This is a phenomenal opportunity for someone early in their career who is passionate about data infrastructure, cloud platforms, and helping others do their best work.

At Wizards of the Coast, we harness the power of imagination and connection to create unforgettable experiences that inspire creativity, spark passion, forge friendships, and foster communities around the globe. Our mission is to inspire a lifetime love of games. Whether through the strategic depth of Magic:
The Gathering®, the rich storytelling of Dungeons & Dragons®, or our AAA digital game studios, we build worlds that bring people together, spark creativity, and fuel adventure.

• Build and maintain shared platform infrastructure, including data pipeline frameworks, reusable templates, and developer tooling.
• Support the administration and day‑to‑day operations of our cloud data platforms (Databricks and Snowflake), including monitoring, access management, and resolving performance problems.
• Help implement and maintain infrastructure‑as‑code (Terraform) for data platform components, following established standards and patterns.
• Assist in evaluating new tools and frameworks, conducting research and proof‑of‑concepts to inform platform decisions.
• Support the optimization and governance of our Databricks and Snowflake environments, including cost monitoring and query performance analysis.
• Help maintain and improve platform capabilities – access patterns, environment configurations, and shared tooling – that enable data and analytics engineers and data scientists to build effectively.
• Partner with analytics and BI teams to solve data platform issues and ensure data products remain accurate and accessible.
• Support the maintenance and monitoring of streaming infrastructure (Kafka and related tooling), surfacing issues and delivering fixes with mentorship from senior engineers.
• Assist in implementing and testing pipeline framework components that enable other teams to build reliable batch and streaming workloads.
• Monitor platform health, respond to alerts, and participate in incident response for data platform issues under the guidance of senior engineers.
• Write and maintain documentation, runbooks, and operational guides that help the broader team maintain platform reliability.
• Participate in post‑mortems and help implement follow‑up improvements.
• Work closely with senior and principal platform engineers to learn platform architecture patterns and standard methodologies.
• Collaborate with data engineering, analytics, and ML teams to understand their needs and help translate them into platform improvements.
• Participate in code reviews and uphold high standards of quality and continuous improvement.

Required

• 2–4 years of experience in data engineering, software engineering, or a related technical role.
• Familiarity with and hands‑on exposure to cloud‑based data platforms.
• Solid understanding of SQL and proficiency in at least one programming language used in data engineering (Python preferred).
• Basic understanding of data pipeline concepts – batch processing, scheduling, data modeling – and a desire to deepen that knowledge.
• Some exposure to infrastructure‑as‑code concepts; hands‑on…