Senior Lead Data Engineer, Content Engineering

About the role

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.

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 that enable unbiased offline training and credible online inference.
• Develop Embedding & Content Pipelines: Build scalable workflows for metadata, imagery, and multimodal representations; partner with Science teams to operationalize new models.
• Architect Data Foundations: Design Delta/Parquet data models and medallion layers, optimizing storage layout and partitioning for latency and cost.
• Real-Time Engineering: Build Kafka-based systems for real-time features and user-activity aggregations, ensuring robust handling of out-of-order events and exactly-once semantics.
• Governance & Leadership: Define data quality rules and schema evolution processes while collaborating across ML pods to translate model needs into infrastructure.

Qualifications

• 7+ years of experience in large-scale data or software engineering.
• Hands-on Expertise: Deep experience with Spark (PySpark/Scala), Databricks, Airflow, and Kafka.
• ML Data Modeling: Proficiency in feature pipelines, temporal joins, and mitigating training-serving skew.
• Cloud Ecosystems: Experience with AWS/Azure/GCP and high-performance engines like Snowflake or Redshift.
• Technical Foundations: Proficient programming skills in Python and SQL with a focus on performance optimization.

Additional Qualifications

• Experience in personalization domains (search, ranking, or recommender systems).
• Experience supporting petabyte-scale data lakehouses or feature stores.
• Familiarity with GenAI/RAG systems, multimodal content, or Delta Live Tables.
• Knowledge of Causal Inference, experimentation signals, or ML evaluation workflows.
• Experience with Terraform for governed, repeatable deployments.

Pay

Hiring Salary Range: $156,800.00 - 235,200.00. The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education.