Engineering Manager, Data Tooling

Job Summary

The Tooling team at Roblox builds the shared tools, frameworks, and developer experiences that make data reliable, discoverable, governable, efficient, and easy to use. Our work spans data quality, lineage, metadata, ownership, observability, lifecycle management, optimization, and self-service workflows that support Data Engineering, Data Science, Experimentation, Machine Learning, and product teams across Roblox.

Responsibilities

• Lead, coach, and grow a team of engineers through clear expectations, regular feedback, thoughtful hiring, and meaningful technical and career development.
• Own and execute a roadmap for shared data tooling, including frameworks for data quality, lineage, metadata, ownership, discoverability, observability, lifecycle management, optimization, and self-service.
• Set technical direction for platform capabilities that turn data and operational signals into actionable workflows, dashboards, alerts, recommendations, and automated remediation.
• Improve the developer and user experience for discovering, validating, operating, and governing data products.
• Drive initiatives that reduce operational toil, improve data quality and freshness, shorten incident resolution time, optimize compute and storage cost, and responsibly retire low-value data assets.
• Create alignment across teams by communicating priorities, technical tradeoffs, risks, dependencies, and measurable outcomes clearly.
• Build a culture of ownership, operational excellence, experimentation, and continuous improvement.

Qualifications

• Experience managing, mentoring, or technically leading engineers, with a demonstrated commitment to developing people and building high-performing teams.
• Experience with data catalogs, lineage systems, data-quality frameworks, observability platforms, or metadata services.
• Familiarity with agentic or AI-assisted workflows for data discovery, analysis, or platform operations.
• Experience building or operating shared platforms and frameworks used by multiple teams—not only one-off datasets or pipelines.
• Experience partnering with Data Science, experimentation, ML, product, or infrastructure teams to translate ambiguous requirements into durable technical solutions.
• Strong product thinking: you can identify high-leverage customer problems, prioritize across competing requests, drive adoption, and measure whether a platform investment is working.
• Clear written and verbal communication, including the ability to explain technical concepts and tradeoffs to both engineering and non-technical stakeholders.