Senior Staff Data Engineer

Welcome to Warner Bros. Discovery... the stuff dreams are made of.

Who We Are...

When we say, "the stuff dreams are made of," we're not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD's vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what's next...

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

Warner Bros. has been entertaining audiences for more than 90 years through the world's most-loved characters and franchises. Warner Bros. employs people all over the world in a wide variety of disciplines. We're always on the lookout for energetic, creative people to join our team.

Your New Role...

• Architect and lead the design of scalable, reliable data pipelines supporting large-scale A/B testing, multivariate testing, and adaptive experimentation.
• Build and maintain systems that support real-time and batch experiment telemetry ingestion, feature logging, exposure tracking, and outcome measurement.
• Design data models and storage strategies optimised for:
  
  • Experiment analysis latency
  • Cost efficiency
  • Long-term reproducibility
  • Governance and auditability
• Enable production-grade pipelines for statistical methods such as CUPED, regression adjustment, and other variance-reduction workflows (in partnership with Data Science).

• Build data infrastructure that supports multi-armed bandit decisioning systems, including:
  
  • Low-latency reward signal pipelines
  • Feature and context streaming
  • Policy logging and replay data stores
• Partner with scientists to productionize bandit frameworks (e.g., Thompson Sampling, epsilon-greedy, UCB) via reliable data services and APIs.
• Design systems enabling off-policy evaluation (OPE), replay simulation datasets, and long-term policy evaluation storage.

• Lead data system architecture supporting the Canvas Optimisation platform, including:
• Artwork / creative performance telemetry
• Impression → engagement attribution pipelines
• Near real-time reward computation
• Global rollout observability and monitoring

• Ensure high availability, correctness, and explainability of decisioning-support data feeds.

• Drive initiatives to reduce overall Labs data processing costs by ~25%, including:
  
  • Query and job optimisation across compute platforms (Databricks, Spark, etc.)
  • Storage tiering and retention policy optimisation
  • Data compaction, partitioning, and indexing strategies
  • Eliminating redundant or low-value pipeline stages
• Establish cost observability dashboards and cost-to-value monitoring frameworks.

• Build reusable data services, libraries, and APIs that:
  
  • Simplify experiment onboarding
  • Standardize telemetry schemas
  • Enable self-service data access
• Define and enforce SLAs for critical experimentation and decisioning datasets.
• Lead data quality frameworks including anomaly detection, reconciliation, and automated validation.

• Serve as a technical thought leader across data platform architecture for experimentation and optimization.
• Mentor data engineers and platform engineers on distributed systems design, pipeline reliability, and performance optimization.
• Influence cross-org roadmaps spanning experimentation science, platform engineering, and product personalization systems.

• BS/MS in Computer Science, Engineering, or related field (or equivalent industry experience).
• 10+ years building and operating large-scale distributed data systems.
• Deep experience with:
  
  • Streaming and batch data architectures
  • Experiment telemetry systems
  • Data modelling for analytics and decisioning
• Hands-on experience with modern data stack technologies (e.g., Spark, Airflow, Redshift, Databricks, Snowflake, Delta Lake, etc.).
• Strong programming mindset with a focus on building and enforcing standards that ensure code is maintainable, readable, and extensible over time.
• Strong communication skills and ability to partner across DS, Engineering, Product, and Analytics.

• Experience supporting experimentation or personalisation platforms at scale.
• Familiarity with adaptive experimentation and bandit system data requirements.
• Experience with cost optimisation of large-scale cloud data platforms.
• Experience operating global, multi-region data systems.