Real-Time Data Engineer – Self-Service Analytics & AI

Paramount; Burbank (CA); On-site; USD 98,400 - 147,600; Full time

Benefits offered by this job

Medical, dental, vision

401(k) plan

Paid time off

Job summary

Paramount is seeking a Data Engineer for the Self‑Service Analytics & Real‑Time Data Platforms team. You will design scalable ETL/ELT pipelines, build semantic layers, and enable governed, self‑service access to trusted data powering BI, analytics, AI apps, and conversational experiences.

Collaborate with Product, Analytics, BI, and Engineering to deliver data products; design data models for analytics and ML; implement observability, governance, and quality.

Qualifications

• 2–4+ years of data engineering experience in production.
• Strong SQL and data modeling for analytics and ML.
• Experience with cloud-native architectures and distributed systems.
• Experience building real-time data pipelines.
• Familiarity with ML data integration and AI platforms.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines.
• Develop semantic layers and curated data products.
• Enable self-service analytics with governed data models.
• Implement monitoring and data quality practices.
• Partner with Product, Analytics, BI, and Engineering teams.
• Drive real-time dashboards and AI-ready data platforms.

Skills

ETL/ELT pipelines

Airflow/Composer

SQL for analytics

Python for data processing

Spark

Real-time data platforms

ML data preparation

Data modeling for analytics

Cloud data architectures

Cross-functional collaboration

Education

Bachelor's or Master's in CS/Engineering or related

Tools

Airflow

Composer

Spark

Vertex AI

Databricks ML

Kafka

Pub/Sub

Superset

BigQuery

Job description

Paramount is seeking a Data Engineer for the Self‑Service Analytics & Real‑Time Data Platforms team. You will design scalable ETL/ELT pipelines, build semantic layers, and enable governed, self‑service access to trusted data powering BI, analytics, AI apps, and conversational experiences.

Collaborate with Product, Analytics, BI, and Engineering to deliver data products; design data models for analytics and ML; implement observability, governance, and quality.