Sr Manager Analytics Engineer

Paramount•West Hollywood, CA

11d•$157,000 - $235,000

About The Position

Paramount Streaming is a division of Paramount that encompasses both free, paid, and premium streaming services including Paramount+ and Pluto TV. We are the Global Content and Lifecycle Analytics team, part of the Paramount Streaming, Data & Insights Group (DIG) team. DIG is a key connector among the Paramount Streaming verticals. The group has subject matter experts. They prototype, build, and scale data infrastructure and products. They assess, aggregate, and analyze data. They also create qualitative and quantitative narratives and insights. This work helps stakeholders make decisions, understand performance, and get business recommendations. In this role you will develop and maintain scalable data pipelines. You will also build reliable core business reasoning. Additionally, you will ensure high data quality standards for content metadata. This includes titles, rights, taxonomy, and catalog data that support Paramount's streaming business. You will work closely with analysts, data engineers, content operations, programming, licensing, and product teams. Your goal is to turn raw metadata feeds and content telemetry into consistent datasets, dashboards, and insights. These resources will support decision-making for Paramount+, Pluto TV, and Paramount's larger streaming portfolio. This role is ideal for someone who thrives at the intersection of engineering and analytics and wants to drive data excellence at scale. This role has people management responsibilities.

Requirements

Bachelor’s degree in economics, Statistics, Analytics, Computer Science, MIS, Data Engineering, or related field.
7+ years of experience in analytical engineering, data engineering, or data analytics with solid SQL and data modeling expertise.
Deep knowledge of ETL/ELT principles, data architecture, and performance optimization.
Proven experience building scalable data products and pipelines using tools such as Databricks, Airflow, Spark, or similar platforms.
Direct experience in modeling and maintaining content metadata pipelines, including managing title catalogs, rights data, windowing data, and genre or talent taxonomies.
Experience with similar structured data in entertainment or media is also acceptable.
Solid ability to collaborate with both technical and non-technical stakeholders to deliver high-impact data solutions.
Fluency in building and maintaining production-grade datasets in cloud-based data environments (e.g., Snowflake, Redshift, Big Query).
Proficiency with BI tools such as Looker and Tableau, including building curated data layers and performance tuning.
Proficient data hygiene practices, including version control, code reviews, unit testing, and documentation.
Exemplary problem-solving skills and attention to detail, especially when reconciling metadata inconsistencies across systems.

Nice To Haves

Experience working with content rights and licensing data, including availability windows, territory restrictions, and content lifecycle management.
Knowledge with content taxonomy and classification systems used in entertainment or media.
Proficiency in Python or another scripting language for data transformation or orchestration.
Knowledge with streaming media or direct-to-consumer digital products.
Experience implementing anomaly detection systems or contributing to root cause investigations for metadata issues.
Knowledge of data governance and compliance best practices as they relate to licensed content.

Responsibilities

Design, build, and maintain robust data pipelines that oversee content metadata, including titles, rights, licensing windows, genres, talent, franchise or series hierarchies, and availability.
Develop and refine reliable data models that unify content metadata from different sources, including ingest, catalog management, licensing, and rights management systems, to aid in precise reporting at the title, franchise, and window levels.
Ensure metadata is complete and reliable by working with software and data engineering teams, catching issues such as duplicate titles, missing or conflicting rights windows, incorrect genre or talent tagging, and misclassified territories.
Contribute to anomaly detection systems and operational dashboards that track content catalog health, metadata completeness, and data quality KPIs.
Collaborate with analysts, content operations, programming teams, licensing, and product managers to turn business needs into technical data solutions.
Build scalable, performant datasets in platforms such as Databricks, Looker, and Snowflake to enable self-service analytics for content stakeholders.
Create and maintain clear technical documents, set coding standards and data governance practices, focus on metadata taxonomy standards, and ensure content rights compliance.