
// Open Role at Tripledot Studios
Join Tripledot Studios as a Data Engineer to develop and support scalable data pipelines, models, and infrastructure for mobile game product analytics. Collaborate with cross-functional teams to build reliable data solutions and explore AI tools to enhance development workflows.
Tripledot Studios is one of the largest independent mobile games companies in the world.
We are a multi-award-winning organisation, with a global 2,500+ strong team across 12 studios.
Our expanded portfolio includes some of the biggest titles in mobile gaming, collectively reaching top chart positions around the world and engaging over 25 million daily active users.
Tripledot’s guiding principle is that when people love what they do, what they do will be loved by others.
We’re building a company we’re proud of. One filled with driven, incredibly smart and detail-orientated people, who LOVE making games.
Our ambition is to be the most successful games company in the world, and we’re just getting started.
As a Data Engineer in our core Data Engineering team, you will help develop and support the data pipelines, models, infrastructure and engineering data products that power product analytics across Tripledot Games. You will work closely with other Data Engineers and collaborate with our BI, Data Science, backend and product teams to build reliable, tested and well-monitored data solutions.
The team’s work spans areas including monetisation analytics, tools that help investigate issues, and a platform for A/B test analysis. You will also have the opportunity to use AI in your day-to-day development workflow and contribute to products built using AI and for AI, including an investigation agent designed to support incident analysis.
Strong SQL and data-modelling skills. Experience building and maintaining data pipelines and working with analytical data platforms. Proficiency in Python for data engineering work. Understanding of analytical and transactional databases, including their differences and appropriate use cases. Experience with a cloud data warehouse such as Snowflake, Databricks or BigQuery. Experience with, or the ability to quickly learn, orchestration and transformation tools such as Airflow and dbt. A practical approach to testing, monitoring and maintaining reliable data systems. Ability to collaborate effectively with technical and non-technical teams. Interest in using AI tools, such as Claude, to improve development workflows and productivity.