Senior Director, Data Engineering & Services

Role Overview:

The Senior Director of Data Engineering and Services is a critical leadership role responsible for the domain-aligned data engineering organizations that turn SIE’s data into trusted, decision and AI-ready products. The role designs, builds, and scales the data assets that power deeper, faster insight and data-informed decisions across Player, Creator, Publisher, and Enterprise experiences, with customer lifetime value (LTV) as the north star.

This role operates within SIE’s data and AI/ML operating model: federated ownership, centralized platform and standards. The domain teams own their data products end to end, building on a centralized, governed lakehouse platform and to a shared set of engineering and governance standards. The objective is insight velocity, reusability, and reliability at scale, not duplicative, team-by-team infrastructure. You will be a primary supply-side engine for SIE’s commercial growth programs spanning acquisition, monetization, and retention, and for the next generation of AI-driven decisioning across the company.

You will oversee data engineering organizations spanning Player Data, Partner Data, and Enterprise Enablement, driving a unified strategy for how SIE designs, builds, and scales its data products.

• Player Data. Define how PlayStation’s player data ecosystem evolves, advancing unified attribution, identity and Player360, and trusted metrics that enable lifetime value modeling, next-best action, causal measurement, and experimentation across player experiences.
• Partner Data. Shape how PlayStation’s partner and creator ecosystem uses data through Partner360, secure data sharing, and self-serve analytics that provide insight into partner performance and lifecycle value, enabling creator success, optimized publishing outcomes, and a stronger PlayStation as the preferred platform for partners.
• Enterprise Enablement. Lead the strategy and execution of enterprise data capabilities that make SIE’s core functions (Finance, IT, Supply Chain, and HR) increasingly data-driven, operating with connected, governed, and intelligence-ready data that enables better planning, forecasting, operational efficiency, and strategic decision-making.

What you'll be doing:

Strategic Leadership

• Define and drive the long-term data engineering strategy that powers SIE’s Connected Data Ecosystem, delivering trusted, high-impact data products aligned to strategic business priorities and to LTV as the north star.
• Operate the federated ownership model in practice: hold the domain teams accountable for data products built on the centralized platform and to central standards, while keeping a clear line between domain ownership and platform stewardship so the organization does not recreate silos.
• Partner with business and product leaders to identify and deliver the most valuable data opportunities across Player, Creator, Publisher, and Enterprise domains, sequencing work against commercial impact.
• Ensure teams build for insight velocity, reusability, and scalability, treating data products as products with clear consumers, contracts, and service levels.
• Develop a high-performance engineering culture built on quality, ownership, test-and-learn, and continuous improvement across global data teams.

Data Engineering Excellence

• Lead the design, architecture, and execution of domain data products on SIE’s centralized lakehouse platform, ensuring seamless integration across business domains rather than parallel, team-specific stacks.
• Deliver decision and AI-ready data pipelines and foundational datasets that power analytics, reporting, personalization, experimentation, and operational intelligence.
• Establish and scale standards for data modeling, integration, processing, quality, lineage, and observability, including consistent, governed staging (medallion) so domains stop hand-building bronze, silver, and gold tables in isolation.
• Lead the modernization of SIE’s data engineering stack, advancing a modern, dbt-based ELT standard on Snowflake and Databricks (with streaming and event-driven patterns on Kafka and Apache Iceberg as the open table format) and migrating legacy pipelines onto it.
• Establish data observability as a first-class platform capability, leveraging modern observability tooling (for example, Monte Carlo) for automated freshness, volume, schema, and quality monitoring, anomaly detection, and end-to-end lineage so issues are detected and resolved before they reach consumers, with clear ownership and SLAs across domains.
• Optimize for data usability, trust, and performance while managing cost efficiency (FinOps) and reliability as engineered properties of the platform, measured through delivery and operational metrics (DORA-style) and product health signals.
• Ensure all data engineering outputs meet enterprise standards for security, privacy, and compliance, with fine-grained access policy (for example, row and column masking and attribute-based access control via Atlan classification and Immuta) applied as a platform capability.

Cross-functional Collaboration

• Partner with the central Data Platform organization and the AI/ML Center of Excellence (hub-and-spoke) so domain data products plug into shared platform, experimentation, real-time decisioning and serving, and productized ML services.
• Collaborate with Analytics, BI, AI/ML, and Platform teams to ensure data assets are discoverable, governed, and integrated into analytical and operational tools through the central catalog and lineage layer (Atlan).
• Work closely with Data Governance, Privacy, and Security teams to ensure responsible data use and compliance with regulatory frameworks, with governance positioned as an enabling standards function rather than an operational gate.
• Drive adoption of data products by ensuring discoverability, documentation, and seamless integration into workflows and decision systems.

Innovation & Efficiency

• Lead initiatives to optimize data operations, improve pipeline efficiency, and reduce total cost of ownership across environments.
• Champion streaming, near-real-time, and event-driven architectures that improve agility and shorten the path from event to insight to action.
• Uplevel the data engineering teams with AI tools, embedding AI-assisted development (pipeline and SQL authoring, code generation, testing, documentation, and incident triage) into everyday workflows to raise productivity, quality, and delivery speed.
• Partner with platform engineering to implement self-serve capabilities that improve practitioner productivity and reduce reliance on bespoke, one-off pipelines.

What we're looking for:

Experience and Technical Expertise

• Proven experience building strong partnerships with product, business, and technology leaders to align on data strategy, accelerate delivery, and improve the strategic value of data across the organization.
• Extensive experience in data engineering or related fields, with deep expertise in designing, building, and scaling enterprise-grade data solutions that enable analytics, experimentation, and machine learning.
• Proven track record of delivering high-quality, reusable, and trusted data products that power decision-making across complex global organizations, ideally within a federated or data-mesh-style ownership model on a shared central platform.
• Deep expertise in data architecture and modeling, with experience designing scalable ELT and ETL frameworks and distributed processing systems that support both batch and real-time workloads.
• Proven success building and modernizing data platforms using Snowflake, Databricks, dbt, Kafka, and open lakehouse formats such as Apache Iceberg, with a strong focus on data quality, lineage, observability, and reliability. Familiarity with modern data observability and quality monitoring tooling is a strong advantage.
• Proven data engineering modernization and migration experience, moving legacy pipelines and tooling onto a modern ELT standard at scale.
• Track record of upleveling data engineering teams with AI tools, raising productivity and quality through AI-assisted development and automation.
• Skilled in cloud-native data ecosystems (AWS preferred; Azure or GCP) and modern governance practices, ensuring performance, scalability, compliance, and trusted access through tools such as Atlan for catalog and lineage and policy engines for fine-grained data access.
• Demonstrated ability to design and deliver domain-aligned data products with clear contracts, service levels, and consumers.
• Familiarity with attribution and identity resolution, causal measurement, and experimentation, and how data engineering enables them at scale.
• Experience working across data privacy and regulatory frameworks, ensuring compliance while supporting global data operations.
• Proven leadership experience building, mentoring, and growing high-performing global engineering teams, fostering a culture of inclusion, innovation, accountability, and test-and-learn.
• Exceptional communicator and collaborator, capable of bridging technical depth and business impact.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers