Senior Director, Reporting & Analytics Engineering

Why Sony Interactive Entertainment

Sony Interactive Entertainment isnt just the Best Place to Play its also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation were 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.

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 youll be doing:

Organizational Leadership

• 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.
• 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.
• Own the organizations budget vendor relationships and contractor or offshore capacity.
• Establish and sustain an engineering culture within an analytics function: peer review automated testing documentation standards source control CI/CD etc.

Analytics Engineering and Data Architecture

• 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.
• Define and enforce dimensional modeling standards transformation frameworks and modular tested version-controlled analytics code.
• Own the enterprise metric layer so that key business measures carry a single governed definition regardless of where they are consumed.
• 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.
• Manage query performance warehouse consumption and platform cost as first-class engineering concerns.

Data Governance Metadata and Lineage

• Own cataloging business glossary data certification and stewardship workflows leveraging tools such as Atlan as the primary metadata platform.
• Maintain column-level lineage across the analytics estate to support impact analysis change management audit and root-cause investigation.
• Leverage data access provisioning and entitlement models including row- and column-level security in partnership with Security Privacy and Compliance.
• Collaborate with Data Engineering to drive data quality monitoring freshness and availability SLAs observability and incident response for analytic assets.

Enterprise Reporting and Data Visualization

• 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.
• Set visualization and information design standards that make reports readable consistent accessible and decision-oriented.
• Rationalize the existing reporting portfolio: retire redundant and unused assets consolidate overlapping content drive down tech debt and drive consumption toward certified sources.
• 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.

Advanced Proactive and AI-Enabled Analytics

• 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.
• 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.
• 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.
• 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.
• Design and deploy analytic agents that proactively monitor data investigate variances assemble narrative explanations and deliver insight into the tools where people already work.
• Shift the organizations 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.

Cross-Functional Partnership Intake and Prioritization

• 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.
• Partner with Analytics Operations to run a transparent intake triage sizing and prioritization process with published capacity explicit tradeoffs and reliable delivery commitments.
• Make capacity constraints visible to stakeholders and executives drive reuse over one-off builds and decline or defer work with evidence rather than friction.
• Serve as a senior executive-facing partner to business functions translating ambiguous business questions into well-scoped analytic solutions.

What were looking for:

• Bachelors degree in Computer Science Information Systems Engineering Statistics Mathematics Economics or a related quantitative field; equivalent professional experience will be considered.
• 15 years of progressive experience in data analytics or business intelligence including 8 years leading teams and 4 years managing managers.
• Demonstrated experience leading an organization of 25 or more people in a large matrixed enterprise.
• Strong hands-on foundation in SQL dimensional modeling and ELT/ETL design with production experience on a modern cloud data warehouse or lakehouse (for example Snowflake Databricks BigQuery Redshift or Synapse) and a transformation framework such as dbt.
• Experience applying software engineering practices to analytics work including Git-based workflows automated testing and CI/CD.
• Enterprise-scale leveraging of BI and data visualization platforms such as Domo Tableau Power BI or Looker including governance provisioning adoption and cost management.
• Demonstrated ability to participate in driving data governance program covering catalog lineage glossary stewardship quality and access using platforms such as Atlan Collibra Alation or Informatica.
• Proven track record of delivering jointly with Data Engineering and Product Management with clear ownership boundaries and shared accountability.
• Direct experience partnering with Data Science including operationalizing visualizing and monitoring model output for business consumption.
• Working command of the statistical concepts underlying exception detection including hypothesis testing confidence intervals variance and control limits seasonality and forecasting.
• Experience running a formal intake prioritization and capacity planning process for a shared services or platform organization.
• Excellent executive communication and influence skills with the ability to operate effectively amid ambiguity and competing priorities.
• Experience managing budget vendor contracts and platform licensing.

Preferred Skills:

• Masters degree or MBA in a related field.
• Production experience delivering LLM-based analytics: retrieval-augmented generation over governed data text-to-SQL or conversational BI semantic layers designed for model consumption and prompt evaluation and cost monitoring frameworks.
• Experience designing agentic workflows and orchestration for monitoring investigation and notification use cases.
• Hands-on experience with Atlan Domo and Tableau specifically.
• Proficiency in Python or R for prototyping and familiarity with common statistical and ML libraries and MLOps concepts.
• Experience deploying anomaly detection at scale including alert routing suppression and fatigue management.
• Familiarity with data product and data mesh operating models domain-aligned ownership and data contracts.
• Experience operating in an Agile or product operating model at scale including portfolio OKR and roadmap management. Deep experience with the Silicon Valley school of Product Management as exemplified by the Marty Kagan-style academic approach a significant plus.
• Experience leading global or matrixed teams including offshore or vendor-partnered delivery models.
• Track record leading a major platform migration consolidation or reporting rationalization effort.
• Demonstrated success attracting developing and retaining senior technical talent in a competitive market.

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Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity gender expression and gender reassignment) race (including colour nationality ethnic or national origin) religion or belief marital or civil partnership status disability age sexual orientation pregnancy maternity or parental status trade union membership or membership in any other legally protected category.

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