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via Greenhouse · 21 September 2026 ·2 days ago

Senior Director, Reporting & Analytics Engineering

PlayStation Global

United Kingdom

This listing is from Greenhouse

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<div class="content-intro">Why Sony Interactive Entertainment?
Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity.</div><h2>Role Overview:</h2>
We are seeking a Senior Director to lead our Enterprise Reporting and Analytics Engineering organization, a team of analytics engineers, report developers, visualization specialists, and people leaders. This organization focuses on the last mile of the enterprise data supply chain: the semantic models, curated data products, metric definitions, and consumption experiences that turn engineered data into decisions.
This is not a traditional reporting leadership role; the classic notion of “reporting” in the form of a myriad of dashboards and filters is racing towards obsolescence. But the need for data and insights, and the need to deliver it in a way that is digestible and actionable, is timeless. Yes, governed dashboards and trusted reporting remain the foundation, and this leader must be excellent at that foundation. But the mandate is to move the organization decisively beyond static reporting toward a proactive, intelligent analytics capability: partnering with Data Science to productize and visualize their models, enabling generative AI and LLM-based access to our data, building exception-based systems that alert users when outcomes deviate from expectation in a statistically meaningful way, and designing agents that monitor data continuously and deliver insight without being asked.
Analytics engineering shares much of its DNA with data engineering — modeling, transformation, testing, version control, CI/CD, performance and cost discipline — but is oriented toward business enablement rather than platform and pipeline. Success in this role therefore depends as much on partnership as on technical depth. This leader will work shoulder to shoulder with Data Engineering on the boundary between platform and consumption, with Product Management on roadmap and requirements, with Data Science on advanced analytic products, and with Analytics Operations on a disciplined intake and prioritization process that makes the best possible use of finite capacity.
The ideal candidate has spent years building the traditional foundations — governance, metadata, lineage, dimensional modeling, engaging with enterprise BI platforms— and is now looking to apply that rigor to a fundamentally different generation of analytic products in new and innovative ways.
What you'll be doing:
Organizational Leadership
<ul>
<li>Lead, coach, and develop an organization of approximately 30 people, including managing through frontline managers; own hiring, role clarity, career pathing, performance management, and succession planning.</li>
<li>Define a multi-year vision and roadmap for enterprise reporting and analytics engineering, and translate it into quarterly outcomes the team and its partners can measure. Partner closely with Product Management to jointly shape the multi-year enterprise end-to-end data strategy.</li>
<li>Own the organization's budget, vendor relationships, and contractor or offshore capacity.</li>
<li>Establish and sustain an engineering culture within an analytics function: peer review, automated testing, documentation standards, source control, CI/CD, etc.</li>
</ul>
Analytics Engineering and Data Architecture
<ul>
<li>Drive maturity of the last-mile architecture in partnership with Data Engineering: curated marts, semantic layers, reusable data products, and certified datasets that serve reporting, ai enablement, data science, and downstream applications.</li>
<li>Define and enforce dimensional modeling standards, transformation frameworks, and modular, tested, version-controlled analytics code.</li>
<li>Own the enterprise metric layer so that key business measures carry a single, governed definition regardless of where they are consumed.</li>
<li>Partner with Data Engineering to define clear contracts and handoffs between pipeline and platform work and last-mile modeling, including shared tooling, standards, and escalation paths.</li>
<li>Manage query performance, warehouse consumption, and platform cost as first-class engineering concerns.</li>
</ul>
Data Governance, Metadata, and Lineage
<ul>
<li>Own cataloging, business glossary, data certification, and stewardship workflows, leveraging tools such as Atlan as the primary metadata platform.</li>
<li>Maintain column-level lineage across the analytics estate to support impact analysis, change management, audit, and root-cause investigation.</li>
<li>Leverage data access provisioning and entitlement models, including row- and column-level security, in partnership with Security, Privacy, and Compliance.</li>
<li>Collaborate with Data Engineering to drive data quality monitoring, freshness and availability SLAs, observability, and incident response for analytic assets.</li>
</ul>
Enterprise Reporting and Data Visualization
<ul>
<li>Drive analytics engagement by leveraging enterprise visualization and provisioning platforms, including Domo and Tableau, in partnership with Data Engineering. Ensure best practices are followed in data architecture, governance, adoption, performance, and total cost management.</li>
<li>Set visualization and information design standards that make reports readable, consistent, accessible, and decision-oriented.</li>
<li>Rationalize the existing reporting portfolio: retire redundant and unused assets, consolidate overlapping content, drive down tech debt and drive consumption toward certified sources.</li>
<li>Build a durable self-service capability through training, templates, community, office hours, and clear guardrails on what belongs in self-service versus centrally managed content.</li>
</ul>
Advanced, Proactive, and AI-Enabled Analytics
<ul>
<li>Partner with Data Science to productize their work: build the visualization, interaction, monitoring, and feedback loops that turn models and research into products the business actually uses.</li>
<li>Partner with Product Management and Data Engineering to enable generative AI and LLM access to enterprise data. This includes preparing the semantic and metadata foundation that makes data legible to models, delivering conversational analytics, text-to-SQL, and retrieval-augmented experiences on governed sources, and establishing guardrails, evaluation, and accuracy monitoring so that answers can be trusted.</li>
<li>Build exception-based analytics that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds.</li>
<li>Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune sensitivity and volume to prevent alert fatigue and preserve signal.</li>
<li>Design and deploy analytic agents that proactively monitor data, investigate variances, assemble narrative explanations, and deliver insight into the tools where people already work.</li>
<li>Shift the organization's consumption model from pull to push: the measure of success is not how many people opened a dashboard, but whether the right person was told the right thing at the right time.</li>
</ul>
Cross-Functional Partnership, Intake, and Prioritization
<ul>
<li>Operate as a true partner to Product Management: contribute to roadmap, requirements, user research, and release planning, and run analytics with a product mindset covering personas, adoption metrics, and asset lifecycle management.</li>
<li>Partner with Analytics Operations to run a transparent intake, triage, sizing, and prioritization process, with published capacity, explicit tradeoffs, and reliable delivery commitments.<

This listing is from Greenhouse. View original listing ↗

Senior Director, Reporting & Analytics Engineering

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