Design and build backend software and data services that turn scattered enterprise and operational data into well-structured, well-governed, easily consumable products. The engineer will work across data modelling, pipelines, APIs and platform services, and will be expected to take a design from concept to a production-grade, reusable implementation with experimentation and iteration approach.

Responsibilities

- Design and build backend services, data models and APIs that are clean, documented, testable and reusable across multiple deployments.

- Develop data pipelines for batch, streaming and unstructured sources; handle ingestion, transformation, quality checks and historisation.

- Model data thoughtfully relational, dimensional, graph or semantic choosing the representation that fits the problem rather than a default.

- Build metadata, catalog and discovery layers so that datasets and services are self-describing: schema, units, granularity, ownership, lineage and access method available programmatically.

- Expose data and capabilities through well-specified interfaces (REST or equivalent, tool/function interfaces for AI agents) with authentication, authorisation and versioning built in.

- Engineer for production: CI/CD, infrastructure-as-code, observability, testing, performance tuning and cost awareness.

- Convert working prototypes into reusable blueprints templates, reference schemas and deployment patterns that other teams can instantiate.

- Partner with domain, business and platform stakeholders to translate requirements into technical design, and document decisions clearly.

- Good to have:



Exposure to industrial or operational data sensor/time-series, alarms and events, maintenance and work-order systems, quality data, engineering documents and to plant asset hierarchies.

- Experience with knowledge graphs, ontologies or semantic modelling.

- Experience grounding LLM or agentic applications in enterprise data, including retrieval, document indexing and tool/API interfaces for agents.

- Prior work standardising a solution across multiple sites, plants or business units.

Requirements

- Solid software engineering fundamentals: data structures, API and system design, modular code, version control, code review, automated testing.

- Proficiency in Python and SQL

- Hands-on experience with a modern cloud data platform or lakehouse, and with distributed processing and streaming frameworks.

- Platform-agnostic design ability matters more than any single product.

- Working knowledge of databases across paradigms relational, document, time-series, graph and vector and the judgement to pick between them.

- Experience with containers, orchestration and CI/CD pipelines in a cloud environment.

- Familiarity with data governance concepts: identifiers and master data, access control, lineage, schema evolution and data quality.

- Clear written and verbal communication; able to document architecture and defend design choices.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Software Engineer, Data & AI Platforms (Mumbai)
🏢 Deeparc Tech
📍 Mumbai