Staff Data Engineer - Product Engagement(Data Platform Team) Hyderabad

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

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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.

Staff Data Engineer - Product Engagement(Data Platform Team) Hyderabad

About Warner Bros. Discovery Warner Bros. Discovery, a premier global media and entertainment company, offers audiences the world's most differentiated and complete portfolio of content, brands and franchises across television, film, streaming and gaming. The company combines Warner Media's premium entertainment, sports and news assets with Discovery's leading non-fiction and international entertainment and sports businesses. For more information, please visit www.wbd.com.

Meet Our Team The Data & Audience organization builds and operates the frameworks, tools, and high-quality data products that power decision-making across WBD, including our flagship streaming product Max. The Product Engagement Data Engineering (PEDE) team owns the multi-tenant, end-to-end engagement data platform that turns tens of billions of daily client events into the trusted, governed datasets behind product analytics, experimentation, machine learning, and weekly business reviews. Our pipelines feed the semantic layer and reporting surfaces that leaders across product, analytics, and data science rely on to grow subscribers, deepen engagement, and make timely, informed decisions.

About the Role We are seeking a Staff Data Engineer to serve as a top technical leader for the Product Engagement Data Engineering team. You will set the technical direction for data systems that process 30+ billion events per day across streaming and batch workloads, and own the architecture of the medallion (bronze, silver, gold) pipelines for sessionization, pathing, user-journey attribution, component performance, and search that produce our canonical engagement datasets. This is a hands-on technical leadership role. You will make the highest-leverage architectural decisions, raise the engineering bar through standards and mentorship, and drive cross-team initiatives that span our shared Scala and Spark code library and our tenant-specific workflows, configurations, and DDLs. You will partner closely with analytics, data science, and platform teams to ensure the engagement data layer is scalable, reliable, cost-efficient, and trustworthy at petabyte scale.

Key Responsibilities Own the architecture of the multi-tenant product engagement data platform end to end, from streaming event ingestion and normalization through silver sessionization and the gold attribution, journey, and component-performance layers that downstream analytics depend on.

Set technical direction and standards for high-performance Spark applications, partitioning and data-modeling conventions, idempotent backfills, schema evolution, and release management across a shared library and multiple tenant pipelines.

Lead the design and delivery of streaming and batch processing in Scala, Spark, and SQL on Databricks and AWS, optimizing for performance, cost, and reliability at scale.

Drive engineering excellence in data quality and observability, including monitoring, alerting, validation, and lineage frameworks that protect the integrity of business-critical engagement metrics.

Resolve the hardest technical problems, including distributed-systems failure modes, session split and late-arrival handling, skew and shuffle optimization, cross-catalog dependencies, and correctness of attribution logic.

Align engineering, analytics, data science, and platform stakeholders on shared contracts such as event schemas, gold-table grains, and semantic-layer mappings, and shepherd new metrics from requirement to reporting surface.

Mentor and grow senior and mid-level engineers through design reviews, code review, pairing, and documentation, and build a culture of rigor, ownership, and continuous improvement.

Evaluate emerging tools and patterns, manage technical debt deliberately, and contribute to the multi-year roadmap for the engagement data platform.

What You'll Bring Experience: 10+ years in data engineering or related software engineering, including a track record as a technical leader on large-scale data platforms.

Programming: Expert-level proficiency in Scala (strongly preferred) and/or Python, with a strong software-engineering foundation including testing, modularity, versioned shared libraries, and CI/CD.

Distributed computing: Deep expertise with Apache Spark (structured streaming and batch), including performance tuning, skew and shuffle optimization, and operating at petabyte scale.

Lakehouse and storage: Advanced experience with Delta Lake and lakehouse or medallion architectures, including schema evolution, partitioning strategy, and idempotent and incremental processing.

Data platforms: Hands-on experience with Databricks (jobs, workflows, clusters) and AWS (S3 and related services).

SQL: Advanced SQL for large-scale transformation, validation, and analytical workloads.

DevOps: Proficiency with CI/CD (GitHub Actions), artifact and version management, and modern build tooling such as sbt.

Architecture and leadership: Demonstrated ownership of architectural decisions for scaling and reliability, plus a record of mentoring engineers and driving engineering best practices.

Preferred Qualifications Background in streaming media, entertainment, or high-traffic consumer applications.

Experience building clickstream or event-based analytics such as sessionization, pathing, user-journey modeling, or attribution at scale.

Familiarity with multi-tenant data platforms and semantic layers such as Looker and LookML, and the contracts between engineering and reporting.

Experience with orchestration and workflow tooling such as Databricks Workflows and Airflow, and infrastructure-as-code practices.

Familiarity with data quality frameworks such as Deequ, and with data governance, lineage, and catalog tooling.

Experience designing and executing large historical backfills with validation and abort criteria.

Exposure to supporting ML and data science consumers such as feature data and experimentation datasets.

Technical Environment Data volume: 30+ billion events processed daily.

Infrastructure: Databricks on AWS (S3, Delta Lake).

Primary languages: Scala, Python, SQL.

Processing: Apache Spark (structured streaming and batch) on a medallion bronze, silver, and gold architecture with real-time and batch layers.

Domain: Product engagement analytics, including sessionization, pathing, user-journey attribution, component (page, rail, tile) performance, search, and weekly business-review metrics.

Platform model: A shared Scala and Spark library of reusable processors plus tenant-specific workflows, configurations, and DDLs serving multiple streaming products.

Engineering practices: Versioned artifact releases, CI/CD via GitHub Actions, unit and integration testing, partition-convention and schema-evolution discipline, and pipeline monitoring and data-quality checks.

What We Offer The opportunity to set technical direction for large-scale data systems serving millions of users worldwide.

A collaborative environment with talented engineers, analysts, and data scientists.

A modern data stack and high technical autonomy.

Direct impact on product strategy and decision-making through trusted engagement data.

A great place to work and an equal opportunity employer.

How We Get Things Done…

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

Championing Inclusion at WBD Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, regardless of sex, gender identity, ethnicity, age, sexual orientation, religion or belief, marital status, pregnancy, parenthood, disability or any other category protected by law.

If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.