Data Engineer MAS Reports

Data Engineer - MAS Reports

Work Mode : Remote

Experience : 5+ Yrs

Location: Banglore/ Pune/ Aemdabad/ Gurgaon/Mumbai

Role overview

The MAS Reports team owns the settlement and reporting layer of the Merchant Acquiring System (MAS). This

batch ETL and reporting platform processes settlement, clearing and fee data, then distributes financial

statements to acquirers, partner banks, merchants and internal Finance and Risk teams.

This role combines hands-on maintenance and incident resolution with the incremental modernisation of an

established, business-critical platform. The successful candidate will be comfortable working across legacy and

modern technologies while taking ownership of data accuracy, report delivery and production stability.

Technology environment

Perl and Shell ETL jobs • cron orchestration • MySQL • SAP BusinessObjects and Crystal Reports • Python • db • Docker • GitHub Actions • Coralogix

Key responsibilities

• Maintain, debug and evolve Perl, Shell and SQL ETL jobs that load settlement, clearing and fee data from the operational MAS database into reporting tables.
• Develop and maintain SAP Crystal Reports and BusinessObjects reports, including their underlying SQL and data logic.
• Contribute to the dbt transformation layer, progressively migrating business logic from legacy Perl and SQL scripts into maintainable transformation models.
• Improve automated testing using pytest, Test::More and coverage tooling, alongside CI/CD controls and production observability.
• Perform root cause analysis of data discrepancies, reconciliation mismatches and other issues affecting financial and transactional reports.
• Investigate and resolve production batch incidents, including failed loads, missed SLAs and report-delivery failures, through the formal Change Request process.
• Participate in the on-call support rota for production batch failures affecting report delivery and downstream stakeholders.
• Collaborate with Engineering, Operations, Finance, Risk and other stakeholders to restore service, communicate impact and prevent recurrence.

Required experience and skills

• Bachelor's or Master's degree in Software Engineering, Computer Science or a related discipline, with at

least four years of relevant professional experience.

• Strong SQL and relational data-modelling skills, including the ability to write, troubleshoot and optimise complex queries against MySQL.
• Solid data-engineering fundamentals, including pipeline design, idempotency, data-quality controls, reconciliation and dependency management.
• Experience building, testing and operating batch ETL pipelines in production environments.
• Practical Python development experience.
• Ability to read and write Perl and Shell, or a demonstrated ability to become productive with them quickly.
• Experience using Docker for local development and test environments.
• Hands-on CI/CD and test-automation practice using GitHub Actions or an equivalent platform.
• Experience with observability platforms such as Coralogix, Datadog or OpenTelemetry.
• Proven root cause analysis involving financial, payment or other transactional data.
• Clear written and verbal communication in English.
• Ability to work effectively in a codebase containing both legacy and modern components, while owning production stability through on-call support and formal Change Request processes.

Nice to have

• Experience with dbt or another data-transformation framework.
• Experience with SAP BusinessObjects, Crystal Reports or comparable enterprise reporting tools.
• Additional software development experience in Java, Scala or Go.
• Exposure to Spark, Kafka, Airflow or comparable distributed-processing and orchestration technologies.
• Experience with cloud data platforms, such as AWS Redshift or Amazon EMR.
• Previous involvement in modernising a legacy data platform by introducing automated tests, CI/CD, observability and maintainable transformation patterns.
• Experience in financial services, payments or another regulated, data-intensive domain.

What success looks like

• Reliable and timely delivery of settlement and financial reports.
• Faster diagnosis and sustainable resolution of production and data-quality incidents.
• Improved automated test coverage, deployment controls and operational visibility.
• Measurable reduction of fragile legacy logic through incremental, well-tested modernisation.