Data Platform Engineer

Hasbro•Renton, WA

4d•$108,300 - $162,500

About The Position

We are 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.

Requirements

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 experience with Terraform is a plus.
Familiarity with Git-based workflows and an understanding of CI/CD fundamentals (GitHub Actions, GitLab CI, or equivalent).
Curiosity, a growth mindset, and a track record of learning new technical skills quickly.
Ability to share information clearly and work together across technical teams.

Nice To Haves

Exposure to streaming concepts or tools (Kafka, Kinesis, or similar).
Hands-on experience with Databricks or Snowflake is a strong plus.
Familiarity with modern data stack tooling: transformation, orchestration, and data catalogs.
Experience with cloud environments (AWS or Azure preferred).
Awareness of MLOps or LLM Ops concepts and the data infrastructure needs of ML teams.
Background in or exposure to data governance, data quality, or observability tooling.

Responsibilities

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.