Data & BI jobs in iGaming

Who is hiring

The most active employers here: Sportradar (46), FanDuel (29), Playtech (18), Sporty Group (13), Betano (Kaizen Gaming) (12). The top five account for 38% of open positions, 88 companies hire in total.

Roles and seniority

Main locations: Remote (64), United Kingdom (59), USA (30), Bulgaria (20). Senior, lead and head roles make up 34% of postings, junior and trainee roles 2%.

Format and languages

Remote: 22% of positions. English is the working language in 97% of postings.

Pay and freshness

8% of employers publish a salary range, so the SpinHire benchmark in «Roles and salary benchmarks» is the practical guide. New in the last 7 days: 18.

The main titles are Data Analyst, BI Analyst, Analytics Engineer, Data Engineer and Data Scientist. Analysts answer business questions, engineers build pipelines and warehouses, and data scientists work on forecasting, churn and risk models.

Sports analytics is a niche of its own. Sportradar, FanDuel and Betano hire people who understand both sport and statistics: probability modelling, line evaluation and analysis of how players bet.

Core product metrics include GGR and NGR, retention, churn, player lifetime value and acquisition cost. Analysts build cohorts, measure the return on bonuses and CRM campaigns, and find where deposits drop out of the funnel.

Other teams depend on data too: fraud teams hunt for multi-accounting, responsible gaming teams flag risky behaviour early, and trading teams watch margin. Understanding how the product works matters as much as writing queries.

A/B testing is a job of its own: landing pages, bonus mechanics, the order of games in the lobby. Analysts help design experiments properly and avoid reading conclusions into random noise.

Strong SQL is expected almost everywhere: window functions, complex joins and query tuning. Power BI, Tableau and Looker are the usual dashboard tools, and Python covers analysis and modelling.

Data engineers benefit from cloud warehouses such as BigQuery, Snowflake or Redshift, orchestration in Airflow and modelling in dbt. Betting and game events often stream through Kafka, so mention it if you have used it.

A typical take-home combines a SQL task on a realistic schema with a business case, such as explaining a drop in deposits or judging whether a bonus campaign paid off. Reviewers look at how you frame and test hypotheses, not just the final number.

On your CV, describe decisions and their business impact rather than listing tools. Experience in e-commerce, fintech or mobile games transfers well, because retention and monetisation metrics work in similar ways.

Data scientists also get questions about models: how you chose features, validated quality and handled a model that failed on new data. Being able to explain results to non-technical people is valued highly.

Ask which team the role sits in: product, marketing, trading, risk or the data platform. That shapes your tasks, stakeholders and growth path. Also ask who owns data quality and whether there is a single warehouse.

Clear stakeholders and access to raw data are good signs. Remote roles are noticeably more common here than in operations, while offices cluster in the UK, Greece, Malta and the US.

Keep regulation in mind. In strict markets, access to personal player data is limited and some work happens on anonymised datasets. That is normal, but it helps to know in advance.