1 month ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build data ingestion, schema management, mapping, transformation, validation, orchestration, monitoring, and operational workflow capabilities.
- Develop scalable APIs, background workers, and execution engines using Python, TypeScript, Go, or equivalent languages.
- Create configuration-driven and metadata-driven frameworks for defining pipelines, business rules, and data mappings without code.
- Build high-throughput data processing capabilities for structured, semi-structured, and flat-file data.
- Implement execution logs, audit trails, data lineage, error handling, retries, SLA monitoring, and data quality checks.
- Contribute to AI-assisted features including schema inference, mapping recommendations, anomaly detection, and pipeline troubleshooting.
- Conduct design reviews, mentor peers, maintain code quality, and promote engineering ownership and robustness.
Requirements
- Experience building scalable backend APIs, workers, orchestration layers, or execution engines in Python, TypeScript, Go, or comparable languages.
- Strong object-oriented design and clean coding practices.
- Solid data engineering fundamentals covering ingestion, transformation, validation, orchestration, metadata management, and data quality monitoring.
- Hands-on experience with relational databases such as PostgreSQL or MySQL and NoSQL technologies such as MongoDB or Redis.
- Familiarity with JSON, XML, CSV, EDI, RESTful API integration, asynchronous processing, message queues, and idempotency patterns.
- Practical experience with workflow orchestration tools such as Apache Airflow or equivalent platforms.
- Ability to translate product requirements into reusable, configurable platform features and collaborate across engineering, product, and design.
- Preferred experience with no-code or low-code platforms, ETL tooling, rule engines, workflow automation, data catalogs, or schema registries.
- Preferred exposure to AI/LLM product development, including agents, function calling, RAG pipelines, structured outputs, evaluation frameworks, and enterprise guardrails.
- Understanding of 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, security, and auditability practices.
- A degree in Computer Science, Software Engineering, or a related technical discipline.
Benefits
- Full-time hybrid work based in Bengaluru, India, with four days per week in the office.
- Meaningful work supporting global supply chain operations.
- Diverse, inclusive, and international team environment.
- Culture of transparency, feedback, continuous learning, and collaboration.
- Opportunities for growth, internal mobility, and professional development.
- Flexible work policies.
Tech Stack
Categories
BackendData Engineering
About Shippeo
Shippeo builds a SaaS platform for real-time, multimodal transportation visibility, providing tracking, predictive ETAs, and transport process automation for enterprise shippers and logistics service providers. The company sells subscriptions and integrations that connect to carrier, TMS, and telematics data to improve on-time delivery and exception management. Founded in 2014 and headquartered in Paris, Shippeo serves global brands such as Heineken, L’Oréal, Coca-Cola HBC, Renault Group, and Saint-Gobain.