Senior Software Engineer (Data)

Grubtech is where food meets technology, and possibilities become reality. We are the unified commerce engine revolutionizing how F&B and quick commerce enterprises connect, operate, and scale in today’s digital-first world. As the backbone behind leading F&B and grocery brands across 18 dynamic markets, we do not just provide technology; we craft experiences that transform how businesses engage with their customers. Our robust integration platform and cutting-edge tools empower enterprises to seamlessly manage omnichannel operations, turning complexity into competitive advantage.

From our headquarters in the UAE to our growing offices in Sri Lanka, Egypt, KSA, and Spain, we are building the future of commerce one innovation at a time. Our expansion into new markets is about bringing transformative technology to businesses ready to redefine what is possible in the digital marketplace.

At Grubtech, we believe that behind every great meal delivery, every smooth grocery order, and every satisfied customer is powerful technology working invisibly to make it all happen. Join us as we continue to shape the future of unified commerce, one market at a time.

The Senior Software Engineer - Data Platform will design, build, and operate Java/Spring Boot–heavy data platform services, Kafka-based streaming workflows, APIs, and data pipelines that power reporting, analytics, operational intelligence, and data-driven products across Grubtech’s unified ecosystem.

Job Responsibilities

• Own the design, development, deployment, and operation of data platform services that support reporting, analytics, operational intelligence, and data-driven products.
• Build scalable Java/Spring Boot services, data pipelines, APIs, and event-driven components that process high-volume transactional and operational data across partners, markets, aggregators, and POS systems.
• Design and maintain reliable Kafka-based streaming workflows for data ingestion, transformation, validation, and delivery.
• Ensure data platform solutions are reliable, secure, observable, performant, and maintainable in production.
• Collaborate with product, engineering, analytics, and operations teams to make data accurate, accessible, trusted, and useful across the organization.
• Apply strong engineering practices, including clean design, automated testing, code reviews, CI/CD, monitoring, and concise documentation.
• Use AI-assisted development, agentic tools, and agent-based workflows to improve delivery speed, quality, testing, debugging, and operational investigation.
• Work with a product-builder mindset by understanding business outcomes, iterating quickly, measuring impact, and continuously improving released solutions.
• Stay current with modern Java, Spring Boot, data engineering, cloud-native platforms, streaming systems, and AI-enabled software engineering practices.

Profile

• Minimum of a bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or an equivalent qualification.
• 3–5 years of engineering experience in backend systems, data platforms, distributed systems, or large-scale production applications.
• Strong hands-on experience with Java and Spring Boot is mandatory.
• Strong understanding of Spring Boot application design, RESTful API development, dependency injection, configuration management, validation, exception handling, and production-ready service development.
• Experience building microservices and backend platform services using Java/Spring Boot in production environments.
• Hands-on experience with Kafka and event-driven systems, including producers, consumers, topics, partitions, consumer groups, retries, dead-letter handling, and failure recovery.
• Experience building or operating data pipelines involving ingestion, transformation, validation, reconciliation, and delivery of large-volume data.
• Working knowledge of Python or Node.js and related frameworks such as FastAPI, Flask, NestJS, Express.js, or similar frameworks is an added advantage.
• Experience with analytical databases or columnar data stores such as ClickHouse, BigQuery, Redshift, Snowflake, or similar technologies.
• Experience with operational databases such as MongoDB, PostgreSQL, MySQL, or similar technologies.
• Hands-on experience with AWS services such as ECS, Lambda, API Gateway, S3, CloudWatch, IAM, and Cognito.
• Good understanding of data quality, schema management, deduplication, observability, and production troubleshooting.
• Experience with CI/CD pipelines, Docker/containerized workloads, and production deployment practices.
• Familiarity with AI-assisted engineering tools, agentic development workflows, or agent-based coding practices is an added advantage.
• Understanding of product-builder concepts such as outcome ownership, user empathy, measurable success metrics, fast iteration, and continuous improvement.
• Communicates effectively in English and can work well in remote and distributed teams.