Data Engineer

Company Profile

RS2 offers secure payment services, payment software and managed services to clients in over 35 countries. The company upholds the highest industry standards and RS2’s in-house designed payments solution is the software of choice by many of the world’s leading and most innovative banks and financial institutions.

About the Job

As a Data Engineer within the AI and Automation division, you will design, build, and maintain the data infrastructure that powers our Business Intelligence and AI initiatives. Your primary mandate is to transform fragmented data sources (source code, Jira tickets, Confluence, task schedules, CRMs, and internal documentation) into reliable, scalable, and actionable data assets.

In this role, you will bridge the gap between technical infrastructure and business value. Working closely with both technical teams and business stakeholders, you will ensure our data accurately captures operational realities and is delivered efficiently to support strategic decision-making, optimize internal processes, and drive commercial growth.

Responsibilities

• Data Pipeline Development: Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and load data from internal systems, APIs, and third-party platforms, carrying out the necessary pre-processing of structured and unstructured data.
• Business Alignment & Impact: Partner closely with cross-functional teams and stakeholders to understand strategic objectives, translating business challenges into scalable technical data solutions and proposing data-driven strategies.
• Data Architecture & Warehousing: Develop and maintain data models, warehouses, and storage solutions that support Business Intelligence, reporting, and AI initiatives, continuously enhancing data collection procedures to capture all relevant information.
• Data Integration: Consolidate data from diverse sources including Jira, Confluence, source code repositories, CRM systems, scheduling platforms, and internal documentation repositories.
• AI/ML Readiness: Prepare structured and curated datasets to support analytics, machine learning, and AI enablement initiatives.
• Data Quality & Governance: Implement data quality controls, monitoring, validation, and lineage tracking to ensure the accuracy, consistency, reliability, and compliance across all datasets.
• Database Optimization: Design and optimize database structures, queries, and storage solutions to improve performance and maintainability.
• Continuous Improvement & Monitoring: Monitor, evaluate, and continuously improve the performance, reliability, and scalability of digital data processes, making necessary adjustments to ensure they meet predefined business objectives.
• Cross-Functional Problem Solving: Actively participate in solution design, technical discussions, and continuous improvement initiatives to solve challenges facing the organization.
• DevOps & CI/CD: Contribute to CI/CD practices, automated testing, deployment pipelines, and infrastructure reliability.
• Documentation & Knowledge Sharing: Maintain clear technical documentation, data dictionaries, and architecture diagrams to facilitate team onboarding and long-term maintainability.
• Analytics & Reporting Support: Support strategic reporting initiatives by delivering curated, business-ready datasets. Work closely with Power BI developers to provide data modelling support, ensure they have a reliable data foundation, and help present results in a clear manner.
• Ad-Hoc Responsibilities: Carry out any other relevant tasks assigned by the Manager of Business Intelligence Solutions.

Must-Have Requirements

• BSc or MSc in Computer Science, Data Science, Analytics, or equivalent practical experience.
• Strong proficiency in Python for data processing, automation, and API integrations.
• Strong SQL skills and experience working with relational databases such as PostgreSQL, MySQL, or Oracle.
• Experience designing, building, and maintaining ETL/ELT pipelines, with a solid understanding of data warehousing concepts, dimensional modelling, and architecture best practices.
• Experience consuming and integrating REST APIs.
• Experience with source control and CI/CD practices using GitLab.
• Implement automated validation checks to ensure historical data integrity and consistency.
• Strong analytical and problem-solving skills, with an awareness of data security, privacy, and governance principles.
• Excellent communication skills in English, both written and verbal, with strong documentation and knowledge management practices
• Research Oriented

Nice-to-Have Requirements

• Experience with workflow orchestration tools (we currently use Apache Airflow).
• Experience with data transformation and modelling tools such as dbt.
• Experience working with Business Intelligence platforms such as Power BI or Tableau.
• Experience supporting AI, machine learning, or generative AI initiatives.
• Experience working with Atlassian products such as Jira and Confluence.
• Experience with containerization technologies such as Docker.