Data Analyst

payabl. empowers businesses to grow through payments innovation and banking services. It is a licensed financial company with principal membership with card schemes, specializing in global payments and providing businesses with multi-currency accounts via its platform payabl.one.

• Strong SQL skills including window functions, CTEs, and aggregation logic with performance reasoning.
• Proficient in Python for analysis with libraries such as pandas, Matplotlib, Plotly, Seaborn, SciPy or equivalent.
• Experience with relational and analytical databases including PostgreSQL; exposure to columnar or OLAP engines such as Apache Druid, ClickHouse, or BigQuery is a plus.
• Experience building dashboards in BI tools like Apache Superset, Metabase, Power BI, Tableau, Looker, or Qlik.
• Comfortable working with notebooks (e.g., JupyterHub, Jupyter) for exploratory analysis and reporting.
• Familiarity with Git-based workflows for version control and code reviews.
• Ability to automate data reports ensuring efficiency and accuracy.
• Excellent communication skills for presenting analysis to various audiences including senior decision makers.
• Strong sense of ownership and end-to-end responsibility.

Nice to have:

• Experience in fintech/payments and understanding of transactional data including chargebacks, fees, interchange, acquiring, and settlement.
• Experience with regulatory or scheme reporting (card schemes, PSD2, AML).
• Experience with dbt for models, tests, documentation.
• Basic understanding of Apache Airflow and pipeline debugging.
• Familiarity with scheduling and automatic report generation (cron, Airflow, jupyter_scheduler).
• Knowledge of event tracking, product analytics tools (Amplitude, Google Analytics, Matomo).
• Practical use of AI tools (LLM-assisted coding, querying, summarizing).
• Exposure to machine learning for business problems like forecasting, anomaly detection, segmentation, fraud, or risk scoring.

• Investigate payment performance issues across gateways, issuers, and merchants, explaining causes.
• Prepare datasets for analysis by filtering, handling missing values, and validating quality.
• Apply statistical techniques to identify patterns and quantify changes.
• Build and maintain Superset dashboards and Druid data cubes.
• Prepare recurring reports for internal and regulatory stakeholders ensuring accuracy and timely delivery.
• Automate recurring analysis and reporting.
• Present insights and recommendations clearly to stakeholders.
• Collaborate with other departments to understand data needs and translate business questions into analytical queries.