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.