Arena Entertainment
Data Analytics Engineer
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About the Role
We are looking for an Analytics Engineer to join our growing data function. In this role, you will focus on transforming raw data into trusted, production-ready datasets used across the entire business. You will take full ownership of our ETL/ELT data pipelines within Snowflake and dbt - designing, building, and optimising them to run smoothly for both real-time and batch data. Ultimately, your work will power our BI reporting suite and lay the groundwork for our future roadmap, where AI agents will leverage semantic layers to provide instant business insights, moving us beyond traditional dashboards.
We need our data function to act as the Subject Matter Expert (SME) for all cross-functional stakeholders (Marketing, Product, Retention, etc.), ensuring they have solid, reliable data to drive their decisions.
Example initial projects include:
- Building out a mature, scalable dbt modeling layer using best practices.
- Implementing AI-driven monitoring for automated data quality and anomaly detection checks.
About Arena Entertainment
Arena Entertainment operates multiple high-growth iGaming and online casino brands within the digital entertainment space. This role will initially focus on our MetaWin and HIT brands, both of which have a strong crypto and Web3 focus.
At Arena, AI is a must-have tool, not a nice-to-have. We expect all of our engineers to natively leverage AI tools (e.g., Copilot, Claude) to code faster, automate repetitive tasks, and work significantly smarter.
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About you
- You are a Team Player. We need you to jam with the wider data team and other departments. Be ready to jump in and help your teammates with reporting or analytics if they're swamped.
- You believe documenting everything is part of the job. Seriously, we're growing fast with lots of brands, so clear docs are essential for keeping things tidy, transparent, and sustainable.
- You love building things right, but also understand the need to be flexible. We gotta clean up tech debt, but sometimes we just need to ship it fast. Be ready to make trade-offs.
- You are not afraid to learn new things. We want you to be curious about how the business works and use your tech skills to solve real-world problems.
- You are a great communicator. You need to talk clearly to both techies and non-tech people. Let us know when you hit a blocker, and don’t suffer in silence!
Our tech stack
- Cloud Platform: AWS (S3, Lambda, DMS, Cloudwatch)
- Data Warehousing: Snowflake, Postgres, Aurora
- Transformation & Modeling: dbt (Core/Cloud), SQL, Python
- Orchestration: Airflow, Dagster
- Data Ingestion (ETL/CDC): Fivetran, DMS, Debezium
- Streaming & Real-time: Kafka, Kinesis
- Infrastructure & DevOps: Terraform, Docker, Kubernetes/Helm, ArgoCD, GitHub Actions
- Data Visualisation (BI): Quicksight, PowerBI
- AI & Productivity: GitHub Copilot, Claude, Gemini
Key Responsibilities
- Design, build, and maintain Snowflake & dbt queries.
- Build scalable ETL/ELT pipelines (using dbt, Airflow, Fivetran).
- Transform raw data into clean, usable models as a single source of truth.
- Integrate different data sources (CRM, payments, games, etc.).
- Own initiatives for data quality improvement and monitoring (e.g., anomaly detection, automated alerts).
- Keep an eye on performance, cost, and security.
- Work closely with cross-functional teams to bridge product development, data, and operations, establishing yourself as the Subject Matter Expert.
- Help us move to real-time data ingestion & ETL using tools like DMS, Kafka, or Kinesis.
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Must haves
- 1-3 years in Analytics Engineering or similar roles.
- Excellent SQL skills.
- Good Python skills.
- dbt experience in production environments (macros, testing, modularisation).
- Practical hands-on experience with AWS.
- Practical ELT design and data warehousing best practices.
- Good CI/CD and Git skills.
- You have used AI coding assistants to work efficiently.
Nice to haves
- Experience optimising Snowflake data warehouses.
- Experience building pipelines to handle high-volume data.
- Experience with ingesting 3rd party data.
- Familiarity with real-time data ingestion.
- Exposure to data science/ML pipelines (SageMaker, Bedrock).
- Used AI tools for monitoring or query optimisation before.
- QuickSight experience (especially SPICE/Direct Query).
- Know the iGaming lingo (GGR, LTV, RTP, acquisition KPIs).
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