Director, Software Engineering - Data Platform, Hyderabad

Director, Software Engineering - Data Platform, 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 new 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 Platform team at Warner Bros. Discovery powers data-driven experiences across global products like Max, Discovery+, Sports, Studios, and News. The team builds and operates a scalable, multi-tenant data ecosystem that enables analytics, real-time insights, and machine learning use cases across the organization. Positioned at the core of the data journey, the team is responsible for ingesting, processing, and transforming large volumes of client and server-side data into reliable and high-quality datasets. Operating at the intersection of data and platform engineering, the team focuses on building robust pipelines, ensuring data quality and governance, and driving innovation in real-time processing, observability, and modern data infrastructure. The team collaborates closely with product, analytics, and engineering stakeholders to enable data-driven decision-making at scale.

Role Summary

• Warner Bros. Discovery is looking for a Director of Engineering to lead critical Data Platform capabilities across data infrastructure, data processing, self-service enablement, governance, metadata, access, reliability, and operational excellence.

• This is a senior engineering leadership role for a leader who can manage engineering managers and senior technical teams, drive platform strategy, improve execution discipline, and operate large-scale systems with strong reliability and cost awareness.

• The role does not require deep expertise in one specific technology or deep machine learning specialization. However, the candidate must be well versed in modern data platform concepts, distributed systems, cloud platforms, engineering operations, and enterprise-grade governance.

Role Scope

• Lead engineering teams responsible for foundational Data Platform capabilities used across analytics, data engineering, business intelligence, operational workflows, and AI/ML enablement.

• Own execution across platform infrastructure, data collection, batch and streaming data processing, metadata, access governance, FinOps, observability, and self-service platform capabilities.

• Drive platform maturity with a focus on reliability, automation, governance, developer experience, security, cost efficiency, and operational readiness.

• Partner closely with engineering, product, program management, analytics, data science, operations engineering, security, and business stakeholders.

Key Responsibilities

Engineering Leadership

• Lead multiple engineering teams and managers responsible for critical Data Platform services.

• Build a strong engineering culture focused on ownership, customer impact, reliability, inclusion, and high standards.

• Coach managers and senior engineers to improve technical judgment, execution quality, stakeholder communication, and operational discipline.

• Create clear operating mechanisms for roadmap planning, prioritization, delivery tracking, risk management, and leadership updates.

• Hire, develop, and retain strong engineering managers, staff engineers, senior engineers, and emerging technical leaders.

Platform Strategy and Delivery

• Translate business and platform strategy into clear engineering roadmaps, milestones, and measurable outcomes.

• Drive modernization of data platform capabilities across storage, compute, orchestration, ingestion, access, metadata, governance, and self-service workflows.

• Balance near-term delivery needs with long-term architecture quality, operational simplicity, and cost efficiency.

• Partner with peer leaders to align priorities across overlapping platform workstreams and reduce duplication of effort.

• Ensure platform investments improve developer productivity, data trust, business velocity, governance, and operational scale.

Operational Excellence

• Establish strong operational practices across owned systems, including SLIs/SLOs, dashboards, alerts, runbooks, incident response, RCAs/COEs, and post-incident learning.

• Improve alert quality, reduce manual toil, and increase automation across platform operations.

• Ensure major launches have clear readiness criteria, monitoring, rollback strategy, ownership, support model, and communication plan.

• Drive a culture of proactive reliability engineering, cost-aware design, and continuous improvement.

• Partner with operational tooling and platform teams to improve observability, cost transparency, anomaly detection, and self-service support.

Governance, Metadata, and Data Trust

• Ensure platform systems support secure, compliant, and auditable access to data.

• Drive adoption of metadata, lineage, ownership, classification, data quality, access review, and lifecycle management practices.

• Improve the discoverability and trustworthiness of data assets through cataloging, documentation, schema management, and data product standards.

• Partner with governance, privacy, and security stakeholders to embed controls into platform workflows rather than treating them as manual afterthoughts.

Self-Service and Developer Experience

• Lead initiatives that reduce manual engineering dependency through self-service onboarding, automated access workflows, reusable templates, and standard platform patterns.

• Improve developer experience for teams building, deploying, operating, and governing data products.

• Create consistent platform standards that help teams move faster while staying within reliability, privacy, security, and cost guardrails.

Cross-Functional Collaboration

• Work closely with product, TPM, analytics, data science, operations engineering, security, finance, and business teams.

• Drive alignment on roadmap priorities, launch readiness, ownership boundaries, operational support, and business impact.

• Represent Data Platform in architecture reviews, roadmap discussions, leadership forums, and executive updates.

• Communicate complex platform topics in a clear, practical, and outcome-focused manner.

Required Qualifications

• 15+ years of professional software engineering experience.

• 5+ years of experience leading engineering managers and large engineering teams.

• Experience operating large-scale distributed systems, data platforms, cloud platforms, or infrastructure platforms.

• Strong understanding of modern data platform concepts such as batch and stream processing, data lakehouse architecture, metadata, governance, access control, orchestration, observability, and cost management.

• Proven ability to lead platform programs across multiple teams and stakeholders.

• Strong operational excellence mindset, including incident management, monitoring, SLOs, runbooks, RCA/COE practices, and production readiness.

• Experience driving self-service platforms, automation, developer productivity, and scalable engineering workflows.

• Ability to make sound technical trade-offs without needing to be the deepest expert in every technology.

• Strong written and verbal communication skills, with the ability to simplify complex topics for senior leadership and cross-functional partners.

• Proven track record of hiring, coaching, and developing managers and senior technical talent.

• Experience working in Agile or continuous delivery engineering environments.

Preferred Qualifications

• Experience with large-scale data platforms using technologies such as Databricks, Snowflake, Apache Flink, Kafka, Airflow, Apache Iceberg, Spark, Kubernetes, AWS, or similar platforms.

• Experience with metadata, catalog, lineage, governance, and data quality platforms such as DataHub, Unity Catalog, Monte Carlo, or similar tools.

• Experience with FinOps, cloud cost optimization, and cost attribution for large-scale data workloads.

• Exposure to AI/ML or GenAI platform enablement, including governance, observability, model or prompt lifecycle, or data readiness for AI use cases.

• Experience leading platform migrations, modernization, or vendor-lock-in reduction efforts.

• Experience supporting data products, semantic layers, data certification, and self-service onboarding.

• Experience working in media, streaming, advertising, consumer product, or high-scale digital platform environments.

What Success Looks Like

• Engineering teams deliver faster with clearer ownership, stronger execution discipline, and fewer escalations.

• Platform capabilities become easier for internal teams to adopt through self-service workflows, automation, and clear standards.

• Production reliability improves through stronger monitoring, runbooks, launch readiness, and incident learning.

• Data assets become easier to discover, trust, govern, and operate.

• Platform costs become more visible, attributable, and actively optimized.

• Cross-team launches have better readiness, rollback planning, and stakeholder alignment.

• Engineering managers and senior engineers grow into stronger leaders.

What we offer

• A Great Place to work

• Equal opportunity employer

• Fast track growth opportunities