Senior Data Analyst

payabl. empowers businesses to grow through payments innovation and banking services. Our ambition is to expand our strong portfolio of global financial services we provide to businesses and make them all available in one place on our platform we call payabl.one. As a licensed financial company with principal membership with card schemes, we specialize in global payments and providing businesses with multi-currency accounts.

The role is about:

As a Senior Data Analyst at payabl, you will work with payment data at scale, including transaction flows, approval rates, chargebacks, fees, and settlement, and turn it into decisions.

You will spend your time answering business questions with data, building dashboards people actually use, and communicating findings. Our data engineers own the platform. You own the analysis and the insight.

You will work with our data stack: Apache Superset for dashboards, Apache Druid/Imply for interactive analytics, Trino over Apache Iceberg on AWS S3 for the data lake, PostgreSQL for operational data, and JupyterHub for exploratory analysis and scheduled reporting.

Location:

Limassol, Cyprus (employment contract)

Remote from Europe (service contract)

Reporting to:

Team Lead Product Analyst

What you will do:

•Investigate payment performance questions end to end, such as approval rate drops and anomalies across gateways, issuers and merchants, and explain why they happened rather than only that they happened

•Prepare datasets for analysis by filtering, handling missing values, and validating quality, so that conclusions rest on accurate and relevant data

•Apply statistical techniques to identify patterns, relationships and trends, and quantify whether a change is real or noise

•Build and maintain Superset dashboards and Druid data cubes that stakeholders rely on daily

•Prepare recurring reports for internal stakeholders and for regulatory and scheme reporting requirements, ensuring accuracy, traceability and on-time delivery

•Automate recurring analysis and reporting so that manual work does not scale with the business

•Present insights and recommendations to stakeholders in a clear and actionable way, backing suggestions with evidence

•Partner with other departments to understand their data needs and translate open-ended business questions into answerable analytical ones

What we need:

•Strong SQL, including window functions, CTEs, aggregation logic, and the ability to reason about query performance on large datasets

•Python for analysis, including pandas, a visualisation library such as Matplotlib, Plotly or Seaborn, and SciPy or an equivalent for statistical work

•Experience with relational and analytical databases, including PostgreSQL. Exposure to columnar or OLAP engines such as Apache Druid, ClickHouse or BigQuery is a strong plus …

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