Senior Software Engineer, Data

Job Description About the Role As a Senior Software Engineer, Data, you will contribute directly to Logward's core product mission by designing and building the data backbone that powers our No-Code Data Platform — enabling users worldwide to ingest, model, transform, validate, monitor, and operationalise data workflows without writing a single line of code. Working at the intersection of backend engineering and data systems, you will partner with product managers, architects, frontend engineers, and QA teams to ship scalable, enterprise-ready platform capabilities that drive real-world supply chain clarity. Location & Work Model: Location: Based in Bengaluru, India. Work Model: Hybrid (4 days per week in-office). Application Requirement: As we operate internationally, we kindly request all candidates to submit their CV in English. Applications submitted in other languages cannot be considered. Contract Type: Full-time Function: Engineering — Data Platform Key responsibilities - Design & Build the Data Backbone: Architect and develop core platform capabilities for data ingestion, schema management, mapping, transformation, validation, orchestration, monitoring, and operational workflows — all in a no-code paradigm. - Deliver Scalable Backend Services: Build robust APIs, background workers, and execution engines using Python, TypeScript, Go, or equivalent, following clean, well-tested, and production-ready engineering standards. - Develop Configuration-Driven Frameworks: Create metadata-driven systems that allow users to define pipelines, business rules, and data mappings through configuration rather than code, enabling broad enterprise adoption without engineering dependencies. - Enable High-Performance Data Processing: Leverage distributed processing frameworks (e.g. Apache Spark, Apache Flink) to build high-throughput ingestion and transformation capabilities supporting structured, semi-structured, and flat-file formats (JSON, XML, CSV, EDI, APIs). - Build Platform Observability: Implement execution logs, audit trails, data lineage tracking, error handling, retry mechanisms, SLA monitoring, and data quality checks to ensure reliability at scale. - Contribute to AI-Assisted Capabilities: Help shape and build AI-agent features including schema inference, mapping recommendations, anomaly detection, and pipeline troubleshooting — with robust tool orchestration, validation guardrails, and human-in-the-loop review. - Champion Engineering Excellence: Conduct design reviews, mentor peers, uphold code quality standards, and contribute to a team culture centred on ownership, robustness, and continuous improvement.