Senior Data Engineer

Position: Senior Staff Data Engineer
This job is with Warner Bros. Discovery, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

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...

We are seeking an exceptional Senior Staff Data Engineer to lead the design, development, and scaling of the data and platform systems that power our experimentation, adaptive optimisation, and automated decisioning ecosystem. This is a high-impact, hands-on technical leadership role that will shape how experimentation data is collected, processed, served, and ope rationalised across millions of users worldwide.

This role will act as a force multiplier for all Labs initiatives and, in particular, will support the development and productionization of the new Canvas Optimisation system.

As a senior technical leader, you will define the architecture and long-term strategy for experimentation data infrastructure, ensure reliable and cost-efficient data pipelines, and partner closely with Data Science, Engineering, Product, and Analytics teams to scale our platform from hundreds to thousands of concurrent experiments and bandits.

This role would be tasked with reducing Labs data processing costs by ~25% over the year through architectural optimisation, storage strategy improvements, compute efficiency, and intelligent data lifecycle management.

Your Role Accountabilities...

Scale Experimentation Data Platforms
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).

Enable Adaptive & Bandit Systems at Scale
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 product ionize 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.

Canvas Optimization & Personalization Infrastructure
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.

Cost Optimisation & Efficiency Leadership
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…