At a Glance
- Tasks: Design and build analytics platforms to support global gaming partners.
- Company: Join Sony Interactive Entertainment, the powerhouse behind PlayStation.
- Benefits: Competitive salary, inclusive culture, and opportunities for growth.
- Other info: Dynamic work environment with a focus on innovation and collaboration.
- Why this job: Make a real impact in the gaming industry with cutting-edge analytics.
- Qualifications: Experience in data modelling, analytics tools, and strong SQL skills required.
The predicted salary is between 60000 - 80000 £ per year.
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.
About the Role
We are seeking a Senior Analytics Engineer to help design, build, and scale our analytics platform. This is a hands‑on, high‑impact role suited to someone who enjoys greenfield analytics work, has strong opinions on data modelling and architecture, and partners closely with stakeholders to translate business problems into trusted, well‑governed data products. You will work within the PlayStation Partners Platform, delivering global data, reporting, and analytics solutions that support partners worldwide in developing and publishing games and content on PlayStation. Our mission is to deliver timely, scalable, and high‑quality data and insights that enable data‑driven decision‑making, improve operational efficiency, and uncover revenue opportunities. In this role, you will help shape how analytics is delivered across the organisation, from modelling standards and dbt best practices to the creation of trusted, decision‑ready datasets.
What You Will Be Doing
- Analytics Engineering and Data Modelling
- Design, build, and maintain analytics‑ready data models using dbt, following best practices such as modularity, reusability, and clear semantic layering.
- Lead the development of greenfield analytics assets, from raw source ingestion through to curated, business‑facing models.
- Apply strong data modelling techniques, including dimensional modelling, facts and dimensions, and other appropriate design patterns.
- Own and evolve the analytics layer architecture, ensuring it scales with data volume and business complexity.
- Ensure data quality and trust through testing, documentation, and observability within dbt.
- Business Partnership
- Partner closely with business stakeholders, analysts, and product teams to understand requirements and translate them into well‑designed data models.
- Act as a trusted thought partner, helping stakeholders understand what is possible with data and shaping requirements collaboratively.
- Balance technical excellence with pragmatic delivery, focusing on measurable business outcomes.
- Platform and Best Practices
- Define and embed dbt standards, including naming conventions, model layering, testing strategies, and documentation practices.
- Contribute to data architecture decisions, including warehouse design, schema organisation, and performance optimisation.
- Improve developer experience through tooling, CI/CD patterns, and modern analytics engineering workflows.
- Maintain deep expertise in the analytics technology stack, ensuring optimal performance and effective feature usage.
Required Skills
- Proven experience delivering data products, analytics, and visualisations using Business Intelligence tools such as Domo, Tableau, Power BI, Qlik, or similar platforms.
- Strong hands‑on experience with dbt, including:
- Data model design and refactoring
- Generic and singular testing
- Documentation and exposures
- Sources, snapshots, and macros
- Excellent data modelling skills across conceptual, logical, and physical models, including both third normal form and dimensional modelling approaches.
- Advanced SQL skills and experience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift.
- Demonstrated ability to design analytics models that are technically robust and intuitive for business users.
- Experience building greenfield analytics or data platforms, or significantly evolving existing ones.
- Experience with data quality and observability, using dbt or complementary tooling.
- Familiarity with orchestration tools such as Airflow, Dagster, or Prefect.
- Experience with semantic layers, metrics frameworks, or analytics consumption patterns.
- Exposure to data governance, access control, or analytics enablement at scale.
- Experience mentoring or supporting junior analytics engineers or analysts.
- Strong track record of partnering effectively with business teams to deliver outcomes.
- Undergraduate degree in a related field, or an equivalent combination of education and experience.
Desirable Experience and Attributes
- Comfort operating in ambiguous or evolving data environments.
- Strong focus on clean design, maintainability, and long‑term sustainability, not just speed of delivery.
- Pride in building trusted data assets that are widely adopted and valued.
- Ability to be opinionated yet pragmatic, clearly explaining trade‑offs and adapting when required.
- Experience maintaining high technical delivery standards, including documentation and end‑user self‑service enablement.
- Collaboration with business, product, and engineering teams to deliver scalable and flexible solutions.
- Ongoing support for users who rely on analytics products for decision‑making.
Nice to Have
- Extensive experience with Domo.
- Hands‑on experience with Snowflake.
- Exposure to web technologies such as JavaScript, HTML, and CSS.
- Experience in web analytics or related domains.
- Knowledge of statistical methodologies.
- Exposure to data science or advanced analytics use cases.
- Experience developing on AWS.
Senior Analytics Engineer employer: PlayStation
At Sony Interactive Entertainment, we pride ourselves on being not just the Best Place to Play, but also the Best Place to Work. Our vibrant work culture fosters innovation and creativity, offering employees exceptional growth opportunities within a dynamic technology environment. With a commitment to inclusivity and diversity, we empower our team members to thrive while contributing to the legacy of the PlayStation brand, making a meaningful impact in the world of entertainment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Sony Interactive Entertainment. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your analytics prowess. When you get that interview, having something tangible to discuss can really set you apart.
✨Tip Number 3
Be ready to talk shop! Brush up on your knowledge of dbt and data modelling techniques. The more you can speak their language, the better your chances of making a great impression.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the team at Sony Interactive Entertainment.
We think you need these skills to ace Senior Analytics Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Senior Analytics Engineer role. Highlight your experience with data modelling, dbt, and analytics platforms like Tableau or Power BI. We want to see how your skills align with what we’re looking for!
Showcase Your Projects:Don’t just list your skills; show us what you’ve done! Include specific examples of analytics projects you've worked on, especially those involving greenfield work or data quality improvements. This helps us understand your hands-on experience.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon unless it's relevant. We appreciate a well-structured application that gets straight to the point, making it easy for us to see your qualifications.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details about the role and our company culture there!
How to prepare for a job interview at PlayStation
✨Know Your Data Modelling Inside Out
As a Senior Analytics Engineer, you'll need to showcase your expertise in data modelling. Brush up on dimensional modelling, facts and dimensions, and be ready to discuss how you've applied these techniques in past projects. Be prepared to explain your thought process behind designing analytics-ready data models using dbt.
✨Showcase Your Business Partnership Skills
This role requires close collaboration with business stakeholders. Think of examples where you've successfully translated complex business requirements into actionable data models. Highlight your ability to act as a trusted partner, helping teams understand the potential of data and how it can drive measurable outcomes.
✨Demonstrate Your Technical Proficiency
Make sure you're well-versed in the analytics technology stack mentioned in the job description. Be ready to discuss your hands-on experience with tools like Snowflake or BigQuery, and your familiarity with orchestration tools like Airflow. This is your chance to impress them with your technical skills!
✨Prepare for Practical Scenarios
Expect to face practical scenarios during the interview. They might ask you to solve a data problem or design a model on the spot. Practise explaining your approach clearly and logically, focusing on best practices in dbt and data quality. This will show that you can think on your feet and deliver under pressure.
