Senior Software Engineer, Data

Logward, a Shippeo company, is a pioneer in AI-powered supply chain execution workflows. As part of Shippeo — the global leader in real-time transportation visibility — Logward brings together trusted, SLA-backed visibility data with intelligent workflows and automation, helping global supply chain teams respond faster, reduce manual effort, and run more connected operations.

*Please note: this role is locally employed by and contracted directly through Logward India Private Limited.*

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

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.

Requirements

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.

Nice to have

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.

Benefits

Meaningful work with real-world impact in global supply chains.; A diverse, inclusive, and international team.; A culture of transparency, feedback, and continuous learning.; Opportunities for growth, internal mobility, and professional development; Hybrid work model and flexible policies.; Preliminary call with a Talent Acquisition Manager; Technical Round 1: Coding & Data Engineering Fundamentals; Technical Round 2: Advanced Data Engineering & System Design; Techno-Fitment round with the Hiring Manager; Final interview