Senior Software Engineer, Data

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.

Proven backend engineering capability — experience building scalable APIs, workers, orchestration layers, or execution engines in Python, TypeScript, Go, or comparable languages, with strong object-oriented design and clean coding practices.

Deep data engineering fundamentals — solid understanding of data ingestion, transformation, validation, orchestration, metadata management, and data quality monitoring.

Database proficiency — hands-on experience with relational databases (PostgreSQL, MySQL) and NoSQL technologies (MongoDB, Redis, or similar).

Data format & integration fluency — familiarity with structured, semi-structured, and flat-file formats (JSON, XML, CSV, EDI) and RESTful API integration, including async processing, message queues, and idempotency patterns.

Workflow orchestration experience — practical use of tools such as Apache Airflow or equivalent platforms.

Collaborative, product-oriented mindset — ability to translate complex product requirements into reusable, configurable platform features, while working effectively across engineering, product, and design functions.

Preferred Qualifications (Nice-to-Haves) Experience with no-code/low-code platforms, ETL tooling, rule engines, workflow automation systems, data catalogs, or schema registries.

Exposure to AI/LLM product development — including agents, function calling, RAG pipelines, structured outputs, evaluation frameworks, and enterprise guardrails.

Understanding of modern data architectures — data warehouses, data lakes, lakehouses, and open table formats such as Apache Iceberg, Hudi, or Delta Lake.

Familiarity with cloud-native development, Kubernetes, distributed workers, event-driven architectures, or object storage.

Knowledge of data contracts, schema evolution, backward compatibility, data lineage, tenant isolation, and enterprise-grade security and auditability practices.

A degree in Computer Science, Software Engineering, or a related technical discipline.