Description
AppsFlyer is a cutting-edge technology company that specializes in mobile attribution and marketing analytics.
The PR&D Analytics & FinOps team helps ensure that product and R&D leadership make data-driven decisions. It supports the organisation by connecting critical data aspects, allowing us to see the bigger picture, providing insights, and leading the organisation in the right direction.
The PR&D Data Analyst is the technical backbone of the team- building the data infrastructure and automation that let our FinOps and product/R&D analytics processes run on AI. This role owns the engineering side of our data pipelines end to end, making sure our data is reliable, well-structured, and ready for both human and AI consumption.
What you’ll do
- Build data processes and structures that make the team's data usable and accessible for AI/LLM consumption.
- Own the engineering and infrastructure of all data pipelines across the team's domains: build, run, and maintain them so they're reliable, well-structured, and performant.
- Design and build automations that convert existing team processes across both, FinOps and product/R&D analytics, into AI-based workflows.
- Serve as the technical connector between the FinOps and product/R&D analytics tracks, supporting both with shared, well-engineered data infrastructure.
- Troubleshoot and optimise existing pipelines and automations, proactively identifying points of failure or inefficiency.
- Collaborate closely with analysts on both tracks to understand data needs and translate them into robust solutions.
What you have
- 3-5 years of experience in data engineering, analytics engineering, or a similar hands-on technical data role
- Excellent SQL skills
- Strong hands-on experience building and maintaining data pipelines (e.g., Airflow, dbt, or similar orchestration/transformation tools)
- Experience with BI/visualisation tools (Looker, Tableau, or similar)
- Hands-on experience working with AI tools (e.g., Claude Code, Cursor, Codex, or similar) as part of a daily workflow, and building AI-consumable data structures
- Degree in Industrial Engineering, or a similar field
- Independent, able to juggle multiple technical priorities across domains simultaneously
- All-in approach with high accountability, a sense of ownership, and passion
- Good communication skills - able to work cross-functionally with both FinOps and product/R&D analysts
Bonus points
- Hands on experience with automation tools (such as Make)
- Proficiency in Python/ other programmatic languages for automation and data processing
- Experience with building AI agent-based workflows