Product Manager, Data Platform

Mattel•El Segundo, CA

1d•$126,000 - $150,000

About The Position

We’re looking for a Product Manager with strong experience in data platforms and integrations to support internal systems at Mattel. This role is focused on customer, marketing, and e-commerce data, with ownership of the end-to-end delivery of data pipelines and integrations into our infrastructure and downstream analytics tools.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Business, Analytics, or related field (Master’s/MBA a plus).
5+ years as a Product Manager (or similar) working on data platforms, integrations, or analytics products (7+ years overall).
Deep technical understanding of modern data stacks:
Data warehousing and integration concepts (modeling, ELT/ETL, DAGs).
Databases and analytics/reporting patterns.
Strong experience owning API-based and file-based integrations (REST APIs, webhooks, batch feeds, SFTP), including vendor/agency data ingestion.
Proven track record building data QA and monitoring:
Data tests, validation workflows, anomaly detection, alerting, incident response.
Comfortable working with engineering teams through delivery (requirements, scope, prioritization, release planning).
Clear communicator who can write specs, run cross-team work, and keep stakeholders aligned.

Nice To Haves

SQL (strong) and/or Python (basic to intermediate).
Google Cloud experience (preferred).
Familiarity with tools/patterns for orchestration, observability, and data QA (any equivalents).
Working knowledge of privacy/security expectations for customer data (baseline handling, access controls, audit needs).

Responsibilities

Own the roadmap for marketing and e-commerce data capabilities across ingestion, transformation, warehousing, and delivery to reporting and activation tools.
Lead buildout and ongoing improvement of our data foundation for marketing and e-commerce data, including source onboarding, modeling, and dataset readiness for reporting and teams.
Drive integration work end-to-end: define requirements, partner with engineering, coordinate with vendors/agencies, manage delivery, and handle rollout and support.
Plan and deliver the sunset of legacy data warehouse solutions, including migration strategy and stakeholder cutover planning.
Own data QA and reliability:
Define data quality checks (completeness, accuracy, freshness, schema changes).
Partner with QA/engineering on automated tests and validation processes.
Set and manage SLAs for key datasets and pipelines.
Put monitoring in place for pipelines and integrations:
Define what we monitor (pipeline health, volume anomalies, latency, failures).
Set up alerting, triage flows, and clear ownership for fixes.
Track issues, manage incident follow-ups, and prevent repeats.
Translate business needs into clear data requirements and technical work:
Define metrics, data definitions, and dataset contracts needed for marketing and e-commerce reporting.
Ensure consistent outputs across sources (web analytics, media platforms, e-comm platforms, CRM/CDP, agencies).
Coordinate across teams (Insights, Engineering, QA, Marketing, Agencies, Platforms) to plan releases, manage dependencies, and keep delivery moving.
Communicate status and tradeoffs clearly to leadership and stakeholders.