Manager, Data Platform Engineering

Hasbro•Boston, MA

4d•Hybrid

About The Position

We are looking for a Manager to lead the team responsible for the infrastructure, platform, frameworks, and tools that power data and analytics across the company. This team builds and operates the shared platform that data engineers, analytics engineers, data scientists, and ML engineers depend on every day, and which provides the critical substrate for an AI native enterprise. This is a hands-on leadership role at an inflection point. The platform has grown organically alongside the business, and we're now ready to be far more intentional about it: fewer, better-supported capabilities; clearer standards; a simpler operating footprint. You'll lead a team of platform engineers through that evolution, setting technical direction, growing the people on the team, and building the organizational alignment that makes real change stick. If you're energized by taking capable platforms and turning them into a coherent product that other teams love using, this role is for you.

Requirements

2+ years managing engineers, plus a strong prior track record as a hands-on data or infrastructure engineer. You should still be able to read a Terraform plan and a bad query execution plan.
6+ years total experience in data engineering, platform engineering, or closely related infrastructure work.
Demonstrated experience leading technical change through influence: bringing stakeholders along, sequencing migrations so no team gets stranded, and sustaining momentum on multi-quarter efforts.
Hands-on depth with modern cloud data platforms.
Direct experience with Databricks and/or Snowflake, and the judgment to evaluate platforms against each other on cost, capability, and operational fit rather than preference.
Strong AWS experience.
Experience operating in — or migrating away from — other major clouds is valuable context.
Practical experience with infrastructure-as-code (Terraform preferred) and Git-based CI/CD workflows.
Solid SQL and working proficiency in Python or a comparable language.
Experience owning production reliability for data infrastructure, including on-call, incident command, and post-incident follow-through.
Practical experience with data access governance: provisioning and deprovisioning, entitlement review, data classification, credential rotation, and audit logging you'd be comfortable putting in front of an auditor.
Working understanding of what AI and ML workloads need from a data platform, including how agents and internal services should get governed, least-privilege access to platform data and tools (MCP or comparable patterns).
Clear written communication. Much of this role is making a technical case legible to people who don't read code.

Nice To Haves

Experience consolidating or rationalizing a fragmented tooling landscape onto a smaller set of supported offerings.
Experience leading a substantial cloud or data warehouse migration, including the change management around it.
Familiarity with streaming infrastructure (Kafka, Kinesis, or similar) at production scale.
Fluency with modern data stack tooling: transformation, orchestration, catalogs, and data quality or observability platforms.
Exposure to MLOps or LLM Ops tooling, or hands-on experience building or governing MCP servers or comparable tool-access layers.
Experience with FinOps practices for data platforms.
Data governance or access management experience at scale in a multi-region or compliance-constrained environment.

Responsibilities

Manage, coach, and develop a team of data platform engineers spanning early-career through senior levels.
Run a healthy engineering practice: clear priorities and process, diligent code review, sustainable support, and blameless retrospectives that produce continuous improvement.
Own hiring and team design as the platform's scope grows, assessing where you need depth or breadth and making great hires.
Create development paths for your engineers, and establish real ownership of the platform and its components within the team.
Treat the platform as a product: define what we offer, who it serves, what's supported, and what isn't.
Establish a set of standard, well-documented platform offerings - the default path most teams should take - and drive adoption across data engineering, analytics, and AI.
Assess the current tooling landscape, identify where capabilities overlap or duplicate one another, and build a prioritized plan to consolidate onto a smaller, better-supported set.
Own the full lifecycle of platform capabilities, including the harder work: deprecating what we no longer want to support and shepherding consumers through migration.
Balance central standards against genuine team-specific needs, and know when to hold the line versus grant a well-reasoned exception.
Develop a clear, defensible point of view on where our data platform architecture should land, and build alignment behind it with engineering leadership and partner teams.
Lead the team through significant platform evolution - consolidating overlapping capabilities, simplifying our cloud footprint, and migrating workloads between environments - with minimal disruption to the teams that depend on us.
Make and document architectural decisions with explicit reasoning on cost, capability, operational burden, and long-term maintainability.
Guide the team's infrastructure-as-code practice (Terraform) so that platform changes are reviewable, repeatable, and reversible.
Own the reliability of the data platform: monitoring, alerting, incident response, and the improvements that reduce recurrence.
Establish the operational metrics that matter — availability, pipeline freshness, query performance, cost per workload — and manage the team against them.
Own platform spend. FinOps is important here - you should understand where the money goes, forecast it credibly, and make deliberate tradeoffs between cost and capability.
Hold a high bar for documentation, runbooks, and operational readiness as a condition of shipping.
Build strong working relationships with data engineering, analytics, BI, AI, and ML teams; understand their workflows well enough to anticipate what they'll need.
Communicate direction, tradeoffs, and timelines clearly to engineers, partner teams, and senior leadership, especially when the answer involves changing how people work today.
Represent the platform's roadmap and constraints in planning conversations, and negotiate scope and sequencing honestly.
Partner with security, IT, and governance functions on access management, data governance, and compliance requirements.