Senior Lead Data Engineer, Content Engineering

Company: Paramount

Location: New York, NY (Remote)

Type: Contract

Remote: Yes

Posted: 2026-06-18

About this role

#WeAreParamount on a mission to unleash the power of content… you in?
We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.


Overview
We are hiring a Senior Lead Data Engineer to build and scale the data foundations that power Paramount’s next-generation personalization systems across Home, Search/Browse, Notifications, and Artwork. This role sits at the core of the Content Engineering vertical, partnering closely with Applied ML, ML Platform, and Causal Science teams to deliver highly reliable, ML-ready data at global scale. You will design and operate pipelines processing billions of daily events, petabyte-scale feature stores, and real-time engagement streams that support ranking and recommendations. This is a high-impact role for an engineer who thrives in distributed systems, large-scale ETL/streaming, and delivering production-grade infrastructure aligned with cutting-edge personalization.


Why This Role Matters
Paramount is investing heavily in a unified personalization operating model. In this role, you will directly shape:

  • The Data Backbone: Building the core of our personalization ecosystem.
  • The User Experience: Defining the feature sets that identify what millions of users view.
  • Innovation Velocity: Enabling ML teams to innovate quickly and safely through high-quality experimentation data.

Key Responsibilities

  • Build & Operate Large-Scale Feature Pipelines: Design and maintain batch/streaming pipelines (Spark, Flink, Databricks, Airflow) producing ML features for ranking models.
  • Ensure Point-in-Time Correctness: Develop feature sets tha...

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