Senior Data Engineer

Company Description

VERSANT is a leading force in news, sports and entertainment – home to iconic and trusted brands that inspire, inform, and delight audiences. Our unique combination of content, technology and services enriches the cultural fabric, igniting passions, sparking conversations, and connecting people to what they love most. As an independent, publicly traded company, VERSANT brings together powerhouse cable networks – including USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!,

SYFY, and Golf Channel – with dynamic digital and direct‑to‑consumer brands such as Fandango, Rotten Tomatoes, Golf Now, Golf Pass, and Sports Engine. Together, these businesses reflect our commitment to delivering exceptional experiences across every screen and service. VERSANT is an industry‑changing media company fueled by innovation and an entrepreneurial spirit. With a strong foundation and a forward‑looking vision, VERSANT empowers creativity, embraces change, and drives connection in an ever‑evolving world.

The Data Engineering team is seeking a Senior Data Engineer to help design, build, and scale the modern data platform that powers analytics, data science, and data products across Versant brands. In this role, you’ll collaborate closely with Data Product, Data Science, Analytics, and Engineering teams to deliver reliable, high‑impact data solutions used by hundreds of internal users. We’re looking for engineers who enjoy hands‑on development, take ownership of production systems, and influence implementation through collaboration and technical expertise.

• Design and implement lakehouse architecture using Delta Lake, including medallion pipeline patterns (Bronze/Silver/Gold), schema enforcement, and time travel
• Build and operate batch and real‑time ingestion pipelines leveraging Databricks Auto Loader, Structured Streaming, and Change Data Capture patterns
• Implement data governance and security using Unity Catalog, RBAC, and compliance‑driven practices for sensitive environments
• Optimize performance and manage costs through Fin Ops strategies, including cluster sizing, workload tagging, Spark tuning, and Photon acceleration
• Design, implement, and maintain CI/CD pipelines and orchestration workflows using Databricks Workflows, Delta Live Tables, and tools such as Airflow
• Collaborate with Data Science teams on ML workflows, including MLflow, feature store integration, and model lifecycle management
• Ensure data quality, observability, and lineage across media‑specific datasets such as streaming logs, ad impressions, and audience metrics
• Provide technical mentorship through code reviews, pairing and knowledge sharing

• Bachelor’s degree in Computer Science, Data Engineering, or equivalent practical experience
• 5+ years of experience building production‑grade data pipelines in cloud environments using Spark‑based platforms (e.g., Databricks, EMR, Dataproc, open‑source Spark)
• Expertise in PySpark, SQL, and Spark‑based data processing, with experience operating pipelines at scale in production
• Hands‑on experience building batch or streaming production data pipelines using distributed processing frameworks (e.g., Spark, Flink) and query engines such as Presto
• Proficiency with orchestration tools such as Apache Airflow or Dagster, with hands‑on experience in CI/CD, monitoring, alerting, and data quality for production systems
• Experience working with modern data architectures, including event‑driven and distributed systems
• Proficiency with Git and collaborative development workflows
• Build and operate batch and real‑time ingestion pipelines using Spark based batch and streaming patterns (e.g., Structured Streaming, CDC), with experience on Databricks or comparable platforms
• Solid understanding of infrastructure, networking, and data security fundamentals

• Experience building Lakehouse platforms and medallion pipelines in Databricks
• Familiarity with Unity Catalog, data governance, and compliance frameworks (e.g., PCI)
• Hands‑on experience with CI/CD pipelines, orchestration tools, and infrastructure‑as‑code
• Experience with Lakeflow Spark Declarative…