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Paramount Global

Joint venture producing film, television content

Data Engineer Data Pipelines and ETL

Data EngineeringUpdated on Nov 14
Full-timeIn-person

About the Job

Requirements

  • 2–4+ years of experience building and scaling ETL/ELT pipelines in production environments.
  • Experience with workflow orchestration tools such as Airflow, Composer, or similar platforms.
  • Strong understanding of distributed data processing concepts.
  • Expert-level SQL skills for large-scale transformation and analytics.
  • Experience designing scalable warehouse schemas and machine-learning-ready data layers.
  • Strong experience optimizing complex queries across multi-terabyte datasets.
  • Proficiency in Python or a similar language for data processing and machine learning pipeline integration.
  • Experience with distributed processing frameworks such as Spark.
  • Experience building real-time data pipelines using Kafka, Pub/Sub, or similar technologies.
  • Understanding of feature streaming, low-latency data processing, and event-driven architectures.
  • Ability to architect and build real-time dashboards using Superset.
  • Experience designing cloud-native data architectures.
  • Experience with lakehouse architectures and cloud data warehouses.
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience.
  • 2–4+ years of experience in data engineering, data pipeline development, or related fields.
  • Strong foundation in modern data engineering principles, distributed systems design, and cloud-native architectures.
  • Demonstrated ability to design and operate large-scale production data systems.
  • Proven track record of technical leadership and cross-functional collaboration.

Responsibilities

  • Design, develop, and maintain scalable batch and streaming data pipelines for large-scale structured and unstructured datasets.
  • Build robust ETL/ELT frameworks supporting analytics, business intelligence, experimentation, and machine learning use cases.
  • Optimize pipelines for performance, reliability, scalability, and cost efficiency.
  • Implement advanced ingestion patterns including change data capture, incremental loads, and event-driven processing.
  • Design scalable, dimensional, and hybrid data models optimized for analytics and machine learning use cases.
  • Develop reusable transformation layers (semantic layers) that serve business intelligence, machine learning, and artificial intelligence applications.
  • Write optimized, production-grade SQL for large-scale analytics workloads.
  • Contribute to query optimization, indexing, partitioning, and performance tuning across distributed systems and cloud warehouses.
  • Build and maintain modular data components following established framework patterns.
  • Contribute to architectural decisions across streaming systems, data lakes, and warehouses.
  • Implement automated data validation, anomaly detection, and monitoring frameworks.
  • Establish data lineage and metadata standards to support reproducibility in machine learning workflows.
  • Enforce governance, privacy, and security best practices, particularly for sensitive artificial intelligence datasets.
  • Ensure responsible artificial intelligence data usage and compliance standards.

Desired Qualifications

  • Experience integrating data pipelines with machine learning platforms such as Vertex AI, Databricks ML, or equivalent; Vertex AI is preferred.
  • Familiarity with vector databases, embeddings pipelines, and AI-serving infrastructure.

About the Company

Paramount Global

Joint venture producing film, television content

Preparing a concise company summary based on the provided description.

Company size
10,001+
Company stage
IPO
Headquarters
Los Angeles, CA
Founded
1912

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