Data Engineer Data Pipelines and ETL

Paramount is seeking a Data Engineer for their Data Engineering team to build and maintain scalable data platforms and ETL/ELT pipelines. The role involves developing batch and real-time data systems that support analytics, machine learning, and AI applications while adhering to modern data architecture and cloud-native best practices.

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, BI, experimentation, and machine learning use cases

Optimize pipelines for performance, reliability, scalability, and cost efficiency

Implement advanced ingestion patterns including CDC, incremental loads, and event-driven processing

Design scalable, dimensional, and hybrid data models optimized for analytics and ML use cases

Develop reusable transformation layers (semantic layers) that serve BI, ML, and AI 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 ML workflows

Enforce governance, privacy, and security best practices, particularly for sensitive AI datasets

Ensure responsible AI data usage and compliance standards

Preferred

Familiarity integrating data pipelines with ML platforms such as Vertex AI, Databricks ML, or equivalent

Experience designing cloud-native data architectures (GCP preferred)

Familiarity with vector databases, embeddings pipelines, and AI-serving infrastructure is a plus

Data pipeline integration

ML platforms

Cloud-native architecture

GCP

Vector databases

Embeddings pipelines

AI-serving infrastructure

Benefits

Medical

Dental

Vision

401(k) plan

Life insurance coverage

Disability benefits

Tuition assistance program

PTO

Bonus eligible

Generous paid time off

Opportunities for both on-site and virtual engagement events

Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace

Company

Global media and entertainment company creating premium content

New York, New York, USA

https://www.paramount.com/

Founded in 1914

10001+ employees

Qualifications

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 ML-ready data layers

Strong experience optimizing complex queries across multi-terabyte datasets

Proficiency in Python (or similar language) for data processing and ML 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 (GCP preferred)

Experience with lakehouse architectures and cloud data warehouses

Bachelor's or Master's degree in Computer Science, Engineering, or 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

Strong problem-solving skills and ability to thrive in complex, fast-paced environments

Detail-oriented and committed to engineering excellence and continuous improvement

Familiarity integrating data pipelines with ML platforms such as Vertex AI, Databricks ML, or equivalent

Familiarity with vector databases, embeddings pipelines, and AI-serving infrastructure is a plus

2–4+ years of experience building and scaling ETL/ELT pipelines in production environments.

2–4+ years of experience in data engineering, data pipeline development, or related fields.

Required skills

ETL/ELT pipeline development, Workflow orchestration tools - Airflow, Distributed data processing, SQL, Data modeling for analytics and ML, Python, Distributed processing frameworks - Spark, Real-time data pipelines - Kafka, Real-time data pipelines - Pub/Sub, Cloud-native data architectures - GCP, Data warehouse architecture, Data quality and governance, Machine learning data integration

Experience

2+ years