Develop, maintain, and improve dashboards, ensuring clarity, consistency, and focus on critical business metrics.
Use SQL extensively to collect, join, clean, and validate data from relational databases and data warehouses.
Actively identify anomalies (sudden drops, out-of-pattern variations, potential fraud, tracking issues, etc.) in revenue, conversion, engagement, and operational metrics.
Define, implement, and evolve monitoring triggers, bots, and automated alerting and KPI tracking routines (daily, weekly, and near-real-time dashboards), quickly signaling relevant deviations to responsible teams.
Perform descriptive, predictive, and prescriptive analyses to understand customer, product, campaign behavior and operational performance.
Create customer segmentations, predictive models (churn, propensity, LTV, risk), A/B tests and statistical models to support decision-making.
Support the dissemination of data best practices, contributing to the organization’s analytical maturity.

Degree completed or in progress in Computer Engineering, Information Technology, Data Science, Statistics, Economics or related fields.
Experience in data analysis or related activities.
Proficiency in SQL for complex queries, joins, aggregations and analysis of large datasets.
Experience with Power BI, including data modeling, DAX, measures, table relationships and dashboard performance.
Knowledge of ETL/ELT, dimensional modeling (star schema) and data ingestion.
Knowledge of GCP (BigQuery and Cloud Storage) or other cloud platforms such as AWS and Azure.
Basic/intermediate Python (pandas) and basic Git for version control.
Analytical mindset and statistical awareness to interpret results, test hypotheses, monitor business metrics and communicate insights clearly to both technical and non-technical audiences.
Will stand out if you have: Experience in iGaming, sports betting, gaming, e-commerce or digital products with high data volume.
Experience with KPI monitoring, anomaly detection and tools for monitoring metrics, events or logs.
Knowledge of applied statistics and experimentation, including A/B testing, hypothesis testing, regression, time series and impact analysis.
Experience with analytic models applied to business, such as forecasting, scoring, clustering or recommendation.
Experience with complex dashboards and exploratory data analysis.
Knowledge or interest in learning dbt, Airflow and data orchestration practices.
Experience with CI/CD practices or data quality automation.

Junior Data Analyst

Key skills