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TODA PayData Analyst
company:Product
sqlpostgresqlpythonpandasaws s3aws glueapache supersetmetabasepower bitableauany one ofapache supersetetl
a/b testingaws iam
> full description
TODA Pay is an international fintech company operating in the digital payments and financial infrastructure space across multiple jurisdictions. We support high-growth payment operations with multi-currency transactions, crypto payments, and complex corporate structures.
The Data Analyst turns payment platform data into clear insights for business and technical teams. The role aims to deliver reliable transaction analytics, identify the reasons behind changes in payment conversion, and support decisions that improve the efficiency and stability of payment services.
The role covers the full analytics cycle: clarifying requirements, preparing data, building BI dashboards, automating reporting, and explaining results. The Data Analyst works on ETL processes in collaboration with data engineers and the IT team.
Technology stack: AWS Glue, Amazon Athena, PostgreSQL, Amazon S3, Python, Apache Superset or similar BI tools.
Requirements:
• Practical experience in data analytics: independently handling business requests, preparing data, validating results, and drawing conclusions.
• Strong SQL skills: complex JOINs, CTEs, window functions, aggregations, date handling, and query optimization in PostgreSQL and Amazon Athena.
• Python for analytics and automation: pandas, data cleansing and validation, reproducible calculations, error handling, and working with large datasets.
• Hands-on skills with Amazon S3 and AWS Glue: storage formats, partitioning, data cataloging, ETL transformations, and validation of data loads.
• Experience building BI dashboards in Apache Superset or similar BI tools, such as Power BI, Tableau, or Metabase: datasets, metrics, filters, visualizations, and clear presentation of metrics for different users.
• Understanding of the payment lifecycle and transaction analytics: authorization, successful and unsuccessful payments, refunds, and retries; selecting the correct denominator for conversion metrics.
• Knowledge of data quality principles and basic statistics; ability to test hypotheses and account for seasonality, traffic mix, and sample limitations.
• Ability to explain findings clearly, align requirements with teams, and read technical documentation in English.
• Awareness of data confidentiality, access controls, and secure handling of sensitive information.
Responsibilities:
• Analyze transaction volumes and values, payment statuses, authorization success rates, payment conversion, declines, refunds, and disputed transactions by merchant, provider, payment method, country, and currency.
• Investigate the payment funnel, distinguish business declines from technical errors, and analyze retries, routing, and the impact of integration changes on payment outcomes.
• Build and maintain dashboards in Apache Superset or similar BI tools to monitor payment metrics; produce regular management and operational reports.
• Write SQL queries in Amazon Athena and PostgreSQL, process data using Python, and develop analytical datasets and data marts.
• Develop and maintain analytical ETL transformations in AWS Glue and data workflows in Amazon S3; coordinate changes with the responsible engineers.
• Monitor data completeness, freshness, and consistency; identify duplicates, missing data, and discrepancies across sources, document their causes, and follow through on corrections.
• Agree on metric definitions, rules for interpreting and counting statuses, time zones, and currency breakdowns; document sources, calculation logic, and analytical limitations.
• Present findings and actionable recommendations, assess the impact of implemented changes, and comply with access rules for payment and personal data.
Nice to have:
• Experience in fintech, payment service providers, acquiring, or payment processing; analysis of merchant and payment provider data.
• Understanding of 3-D Secure, settlement, chargebacks, and reconciliation; experience reconciling transaction and financial data.
• Experience analyzing payment routing, A/B tests, and causes of declining conversion; collaboration with risk and antifraud teams.
• Experience with Git, ETL orchestration, data load monitoring, automated data checks, and analytical data mart modeling.
• Knowledge of Parquet, query cost optimization in Athena, AWS IAM principles, and data protection in cloud environments.
We offer:
The Data Analyst turns payment platform data into clear insights for business and technical teams. The role aims to deliver reliable transaction analytics, identify the reasons behind changes in payment conversion, and support decisions that improve the efficiency and stability of payment services.
The role covers the full analytics cycle: clarifying requirements, preparing data, building BI dashboards, automating reporting, and explaining results. The Data Analyst works on ETL processes in collaboration with data engineers and the IT team.
Technology stack: AWS Glue, Amazon Athena, PostgreSQL, Amazon S3, Python, Apache Superset or similar BI tools.
Requirements:
• Practical experience in data analytics: independently handling business requests, preparing data, validating results, and drawing conclusions.
• Strong SQL skills: complex JOINs, CTEs, window functions, aggregations, date handling, and query optimization in PostgreSQL and Amazon Athena.
• Python for analytics and automation: pandas, data cleansing and validation, reproducible calculations, error handling, and working with large datasets.
• Hands-on skills with Amazon S3 and AWS Glue: storage formats, partitioning, data cataloging, ETL transformations, and validation of data loads.
• Experience building BI dashboards in Apache Superset or similar BI tools, such as Power BI, Tableau, or Metabase: datasets, metrics, filters, visualizations, and clear presentation of metrics for different users.
• Understanding of the payment lifecycle and transaction analytics: authorization, successful and unsuccessful payments, refunds, and retries; selecting the correct denominator for conversion metrics.
• Knowledge of data quality principles and basic statistics; ability to test hypotheses and account for seasonality, traffic mix, and sample limitations.
• Ability to explain findings clearly, align requirements with teams, and read technical documentation in English.
• Awareness of data confidentiality, access controls, and secure handling of sensitive information.
Responsibilities:
• Analyze transaction volumes and values, payment statuses, authorization success rates, payment conversion, declines, refunds, and disputed transactions by merchant, provider, payment method, country, and currency.
• Investigate the payment funnel, distinguish business declines from technical errors, and analyze retries, routing, and the impact of integration changes on payment outcomes.
• Build and maintain dashboards in Apache Superset or similar BI tools to monitor payment metrics; produce regular management and operational reports.
• Write SQL queries in Amazon Athena and PostgreSQL, process data using Python, and develop analytical datasets and data marts.
• Develop and maintain analytical ETL transformations in AWS Glue and data workflows in Amazon S3; coordinate changes with the responsible engineers.
• Monitor data completeness, freshness, and consistency; identify duplicates, missing data, and discrepancies across sources, document their causes, and follow through on corrections.
• Agree on metric definitions, rules for interpreting and counting statuses, time zones, and currency breakdowns; document sources, calculation logic, and analytical limitations.
• Present findings and actionable recommendations, assess the impact of implemented changes, and comply with access rules for payment and personal data.
Nice to have:
• Experience in fintech, payment service providers, acquiring, or payment processing; analysis of merchant and payment provider data.
• Understanding of 3-D Secure, settlement, chargebacks, and reconciliation; experience reconciling transaction and financial data.
• Experience analyzing payment routing, A/B tests, and causes of declining conversion; collaboration with risk and antifraud teams.
• Experience with Git, ETL orchestration, data load monitoring, automated data checks, and analytical data mart modeling.
• Knowledge of Parquet, query cost optimization in Athena, AWS IAM principles, and data protection in cloud environments.
We offer:
- Opportunity to work in a fast-growing international fintech company
- Professional growth opportunities alongside with company
- 24 paid vacation days per year and 5 paid sick leave days per year
- 10 Public holidays day
- An opportunity to work full remote or hybrid flexibility
- Collaborative and friendly team
- Professional education budget after 6 months working in the company
- Birthday gift bonus for all our employees