Data Engineer

About the company

The employer is a global payments technology leader operating across more than 200 countries and territories. Its focus is on enabling secure, seamless transactions between consumers, merchants, financial institutions, and public sector organizations. The organization emphasizes large-scale impact, innovation, and meaningful work that supports communities and customers worldwide.

Role overview

This Data Engineer position for the Asia Pacific region is based in Bengaluru and sits at manager level. The role calls for strong expertise in big data and data warehousing, along with the ability to design and operate large-scale data processing systems using modern database and data engineering technologies. It supports regional data platforms used by data scientists, analysts, and BI users to build solutions for clients.

The role involves owning data pipelines, local warehouse design, data marts, and reusable data frameworks, while also supporting different ways of presenting and consuming data. The ideal professional is technically strong, proactive, energetic, and comfortable solving complex problems at scale.

Key responsibilities

• Build, operate, and improve high-quality data pipelines, data marts, and related architecture in line with the global technology stack.
• Work closely with the global data engineering function to align APAC practices with enterprise engineering standards.
• Create custom packages that help data science teams across the region do their work more effectively.
• Support the development of new data science platforms and tools using both on-premises and cloud-based technologies.
• Find and implement process improvements that increase scalability across existing client solutions.
• Partner with business stakeholders to address client and consulting needs related to data and infrastructure.
• Work with technology teams during quarterly planning and help track performance, stability, and reliability using relevant metrics.
• Improve infrastructure efficiency by reviewing query logs, tuning configurations, and translating queries when needed.
• Review scripts against best practices and help build training material for beginner and intermediate users.
• Strengthen consistency in tool usage by creating usage guidance for specific data science applications and sharing it with the user community.

What the role requires

• Strong hands-on experience in API development, API publishing, and API integration.
• Deep experience in data engineering with understanding of distributed data architecture, BI tools, and machine learning packages.
• Good command of Hadoop architecture and core components such as HDFS, YARN, and MapReduce.
• Solid SQL knowledge and exposure to multiple database systems.
• Ability to build production software and systems in Python and/or Scala.
• Track record of diagnosing and fixing performance bottlenecks in production environments.
• Experience working with large, complex, multi-dimensional datasets and machine learning models across unstructured, structured, and streaming data.
• Working knowledge of generative AI, related tools, and LLMs; hands-on experience is preferred.
• Ability to quickly learn new tools and approaches as the data science landscape evolves.
• Proven capability to apply new techniques to solve business problems.
• Strong communication and presentation skills, including the ability to work with cross-functional teams at different levels.
• Self-motivated, results-driven approach with the ability to manage several projects at once.
• Hands-on experience with cloud services such as AWS, including designing and building data pipelines on AWS.
• Knowledge of the payments or banking industry is an added advantage.
• Hadoop certification from Cloudera or Hortonworks and Apache Spark certification are preferred.
• Understanding of data visualization tools such as Tableau or Power BI.
• AWS cloud certification at intermediate or advanced level is desired.
• Graduation is not mandatory for this opportunity.

Work model

This is a hybrid role. The exact number of in-office days will be confirmed by the hiring manager, and the expectation is at least 3 days per week in the office.

Equal opportunity statement

The employer considers qualified applicants without discrimination on the basis of race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. Candidates with criminal histories will also be reviewed in line with relevant local law and EEOC guidance.