Principal Data Engineer

Univision•Los Angeles, CA

About The Position

TelevisaUnivision is seeking a Principal Data Engineer to provide technical leadership for the design, development, and deployment of its data platform supporting the ViX streaming service. The Data Engineering team builds and operates high-volume, high-velocity pipelines for playback and engagement events, subscriptions, advertising, and the analytics data warehouse. The role will set architecture and multi-year technical direction, remain hands-on with complex real-time and batch data challenges, and mentor engineers across the team. The Principal Data Engineer will build reusable frameworks and reference implementations, lead real-time pipeline design, own pipelines through production support, and promote data quality, reliability, security, observability, governance, and cost efficiency. The role partners with Product, Data Science, Analytics, and Engineering teams, contributes to platform strategy, leads responses to severe incidents, and helps adopt emerging technologies, including AI-assisted engineering and LLM-enabled data tools while maintaining quality and security standards. Applicants must be authorized to work in the United States on a full-time basis. Employment and education will be verified.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent experience.
12+ years of experience in Data Engineering, Data Warehousing, or Big Data environments.
At least 2 years in a tech-lead role setting technical direction across teams.
Demonstrated experience mentoring and developing junior and mid-level engineers.
Proven experience designing and operating high-volume production real-time data platforms, such as platforms processing billions of events per day.
Expert-level experience with GCP, especially BigQuery, Pub/Sub, Dataflow (Apache Beam), Cloud Composer (Airflow), and Cloud Storage; AWS experience will also be considered.
Expert-level Python and SQL skills and production experience with dbt.
Deep knowledge of stream processing concepts, including event time versus processing time, windowing, watermarks, exactly-once delivery, and backpressure; experience with Kafka, Flink, or Spark.
Expert understanding of analytics data modeling, including dimensional modeling, event modeling, and lakehouse table formats such as Apache Iceberg.
Strong experience with Infrastructure as Code tools such as Pulumi or Terraform, CI/CD, and automated testing for data pipelines.
Demonstrated ownership of data reliability, including SLOs, data observability, incident management, and postmortems.
Demonstrated success reducing cloud data platform costs at scale.
Excellent written, verbal, and presentation skills, including design documentation and communication with technical and executive audiences.
Must be authorized to work in the United States on a full-time basis.
Employment and education will be verified.

Nice To Haves

Experience in subscription, streaming video (OTT), media, or sports organizations, including playback quality, engagement, and ad-supported (AVOD) data.
Experience supporting machine learning and data science workloads, including feature pipelines, real-time features, recommendation, and personalization data.
Experience with data contracts, schema registries, and data catalog or lineage tools.

Responsibilities

Build reference implementations, frameworks, and reusable libraries for ingestion templates, dbt and Airflow patterns, and Pulumi infrastructure modules.
Lead the design of real-time pipelines on Pub/Sub and Dataflow, including event schemas, schema registry and evolution, exactly-once processing, watermarking, and late-data handling.
Own assigned pipelines from design and development through testing, deployment, and production support.
Ensure data quality, integrity, security, and availability across the data ecosystem.
Partner with Product Managers, Data Scientists, Analysts, and Engineering teams to translate business requirements into technical solutions.
Contribute to data architecture discussions and long-term platform strategy.
Identify opportunities for process improvement and operational efficiency with stakeholders.
Optimize data storage, processing, and orchestration for performance, scalability, and cost efficiency, including BigQuery query and storage costs.
Monitor, troubleshoot, and resolve pipeline and platform issues, and contribute to incident reviews.
Implement best practices for data governance, observability, testing, and CI/CD.
Lead response to severe data incidents, conduct blameless postmortems, and drive systemic fixes.
Mentor junior data engineers through technical guidance, code reviews, and knowledge sharing.
Contribute to architectural decisions and engineering best practices.
Participate in technical interviews and help grow the data engineering team.
Evaluate and drive adoption of emerging technologies, including AI-assisted engineering workflows and LLM-enabled data tooling, while maintaining quality and security standards.