Senior Software Engineer Data Platform

Location: Bangalore, India

Reports to: Development Manager

Role summary

We are looking for a Senior Software Engineer to design, build, and scale our next-generation Data Platform and Data-Driven APIs. This role combines distributed data processing (Apache Spark) with platform and microservices engineering (Java) to enable reliable, scalable, and real-time data access.

You will operate at the intersection of data engineering and backend platform engineering-building systems that not only process large volumes of data but also expose that data through robust, well-designed APIs and services.

This role goes beyond implementing requirements. We expect engineers to understand business context, challenge assumptions, and take end-to-end ownership of delivering meaningful outcomes.

Key responsibilities

- Design and develop scalable data pipelines using Apache Spark (batch and streaming)
- Build and maintain data platform layers: ingestion, transformation, and serving
- Optimize Spark jobs for performance, cost, and reliability (partitioning, skew handling, memory tuning)
- Implement data quality, observability, and lineage frameworks
- Contribute to data architecture decisions (Lakehouse, data mesh, storage formats, partition strategies)
- Define and enforce data contracts and schema evolution practices
- Design and build data-driven platform APIs using Java (preferred)




- Develop microservices that expose curated datasets for product and partner consumption
- Implement RESTful APIs and event-driven services for real-time and near real-time data access
- Ensure low-latency, high-availability data serving layers
- Integrate with upstream/downstream systems, including legacy APIs where required
- Build and deploy solutions on Azure (preferred) / AWS / GCP
- Leverage cloud-native services for data storage, compute, and messaging
- Work with event streaming systems (Kafka/Event Hubs) for real-time pipelines
- Support containerized deployments and orchestration (Kubernetes) where applicable

Quality, Observability Engineering Excellence
- Champion unit tests across both data and service layers
- Build automated validation frameworks for data pipelines
- Implement end-to-end observability (metrics, logging, tracing) across pipelines and APIs
- Drive CI/CD practices for both data and application code
- Conduct code reviews and enforce engineering best practices

Product Mindset Ownership
- Engage deeply with product and business stakeholders to understand why, not just what
- Translate business problems into scalable data and platform solutions
- Take end-to-end ownership from design through production and support
- Proactively identify performance bottlenecks, data issues

📌 Expert Software Engineer, Data Platform (Bengaluru)
🏢 Alegeus
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