Middle Data Analyst

Role Overview:

We’re looking for an experienced Middle Data Analyst to join analytics team in the iGaming industry. You’ll take ownership of complex analytical tasks, drive data- informed decisions across multiple business functions, and serve as a go-to expert for data quality, player behaviour analytics, and reporting. If you have a solid background in gambling analytics and love working with large datasets, we want to hear from you.

Key Responsibilities:

Design and own end-to-end analytical workflows in Amazon Redshift — complex multi-step SQL: window functions, CTEs, large-scale joins, and performance tuning;

Conduct deep-dive player behaviour analyses: segmentation, RFM modelling, cohort analysis, LTV forecasting, and churn prediction;

Analyse A/B test results — interpret statistical significance, evaluate group comparability, identify confounding factors, and deliver clear recommendations to stakeholders;

Track and analyse iGaming KPIs — GGR, NGR, bonus abuse index, deposit/withdrawal trends, player LTV, and churn — proactively flagging anomalies;

Integrate and actively apply AI tools into day-to-day analytical work: prompt engineering, AI-assisted SQL generation, automated insight summarisation;

Translate complex data findings into clear, actionable outputs for non- technical stakeholders via Excel reports and presentations;

Learn and work in EasyMorph for reporting and data preparation tasks as part of team onboarding.

Required Skills & Experience:

Technical skills:

3–4 years of hands-on experience as a Data Analyst in iGaming or online gambling — mandatory;

Advanced SQL in Amazon Redshift — window functions, CTEs, subqueries, query optimisation, and working with large-scale datasets;

Experience with BI tools (Power BI, Tableau, Looker, or similar) - working with dashboards and using them to extract insights;

Proficiency in Excel: advanced pivot tables, vlookups and executive-level reporting;

Strong command of iGaming KPIs: GGR, NGR, player LTV, churn rate, bonus abuse metrics, deposit/withdrawal volumes;

Solid understanding of A/B test analysis — interpreting p-values, confidence intervals, and effect sizes; experience evaluating pre-test group balance and flagging randomisation issues;

Practical use of AI tools in analytical workflows: LLMs for insight generation, AI-assisted querying, prompt engineering for data tasks;

Strong data quality mindset — ability to sense-check numbers, investigate inconsistencies, and ensure analytical accuracy;

Clear English communication, written and verbal (upper-intermediate+).

Nice to Have:

Familiarity with Amazon QuickSight: navigating dashboards, using built-in features (Q / flow), filters, and calculated fields;

Exposure to EasyMorph or similar ETL/data preparation platforms.

Soft Skills:

High ownership and autonomy — you manage your tasks end-to-end without hand-holding;

Structured analytical thinking — you break down ambiguous business questions into clear data problems;

Proactive communication — flagging anomalies, pushing back on flawed assumptions, and keeping stakeholders aligned;

Ability to manage multiple priorities and deliver under deadlines in a fast-paced environment;

Collaborative mindset — comfortable working across teams and supporting junior colleagues.

The company guarantees you the following benefits:

A positive workplace atmosphere that creates a culture of collaboration and support, making it a place you'll love working in;

Competitive compensation and regular career development reviews;

Flexible working hours and remote working options, you'll enjoy the freedom that the company provides;

A generous vacation and sick leave policy, allowing you to take time off and enjoy a work-life balance;

Financial assistance for professional development, helping you stay ahead of the curve and love your career path;

Educational Allowances that give you the opportunity to expand your knowledge and experience;

You'll have a monthly allowance for personal activities, giving you the opportunity to pursue your interests and hobbies outside of work;

A comprehensive health insurance plan, depending on your current location;

Referral program with financial rewards for bringing top talent to the company;

Engaging in team-building activities and corporate parties.

Interview process:

HR Interview with the Recruiter;

30 minutes’ technical interview;

1.5-hour Final interview with the team;

Final decision.

If you find this opportunity right for you, don't hesitate to apply or get in touch with us if you have any questions!