Data Applications Engineer
Posted 13 Oct 2026
Advertised as “Senior Data Applications Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
No salary listed for this role.
About WPT Global
Since WPT’s inaugural high-stakes televised poker games in 2003, the brand has symbolised poker prominence. Today, WPT Global is building the next generation of online poker and gaming experiences for players around the world.
The Role
We’re looking for a Senior Data Applications Engineer to join our small, all-senior data team.
We build the data models, pipelines and applications that support the business, from product and marketing analytics through to fraud detection and internal operational tools.
We work AI-first. Coding agents write a significant amount of our code, while we focus on architecture, testing, guardrails and making sure AI-assisted development is safe and reliable in production.
You’ll help turn our data into useful applications, APIs and internal tools, not just tables and dashboards.
Requirements
- 5+ years of experience building production software across backend and frontend.
- Strong AI-first development experience, using tools such as Claude Code, Codex or Cursor regularly.
- Deep Python backend experience, including APIs, authentication and testing.
- Strong TypeScript / React experience.
- Strong SQL and data modelling skills.
- Hands-on data engineering experience with warehouses or lakehouses, pipelines and dbt.
- Experience with containers, CI/CD and cloud deployment. AWS preferred.
- Strong technical judgement and the confidence to challenge designs and propose better solutions.
Responsibilities
- Build data applications end to end using AI, from data models and infrastructure through to APIs and user interfaces.
- Turn data into internal tools, APIs and self-service products for teams across the business.
- Work across Databricks workspaces, dbt projects and data pipelines.
- Help shape technical direction across application architecture, APIs, data modelling, testing, security and performance.
- Review AI-generated code, identify weaknesses and turn recurring issues into automated checks and guardrails.
- Improve our codebases and workflows so both engineers and coding agents can work more effectively.
Nice to Have
- Experience building agents or LLM-powered tools, including MCP, orchestration or evals.