Engineering Analytics - Lead Engineer, Smart DC

We are looking for highly skilled and hands-on analytics engineer who can take end-to-end ownership of the UI, APIs, and IoT data pipelines for our predictive intelligence platform. This role requires deep technical expertise across frontend, backend, and data engineering, with strong accountability for delivering high-quality, production-ready solutions.

Responsibilities

• UI Development: Build and enhance user interfaces using ReactJS (or FJS) with a focus on modern, intuitive, and responsive design

• Data Pipeline Ownership: Own the complete data acquisition pipeline including:

  - IoT modules

  - Azure IoT Hub & Event Hub

  - Data Bricks cluster (data processing & transformations)

  - Distributed Postgres/Citus database clusters for storage

• API Development: Design, develop, and maintain scalable RESTful/GraphQL APIs.

• Full Software Development Lifecycle (SDLC) Ownership: Take responsibility for the end-to-end lifecycle of components — from requirements, design, coding, testing, deployment, and support.

• Quality & Best Practices:

  - Follow DevOps and software engineering best practices

  - Write and maintain unit, integration, system, and end-to-end tests

  - Participate in and respect the code review process

• Collaborative: Ability to work collaboratively with Subject Matter Experts (SMEs), architects as well as other developers in the gathering of requirements and explanation of technical solutions

• Product Mindset: Focus on delivering core product value rather than standalone solutions.

Job Requirements

• Proven 6 - 8 years of experience as a full stack and analytical engineer with ownership of production-grade systems

• Strong hands-on skills in:

  - ReactJS (or similar modern JS frameworks)

  - Backend development (Node.js, Python, or equivalent)

  - IoT integration and Azure IoT Hub/Event Hub

  - Data engineering (Data Bricks, data pipelines, distributed databases such as Postgres/Citus)

• Strong understanding of distributed systems, scalability, and data modeling

• Strong knowledge of DevOps practices: CI/CD, monitoring, automated testing

• Excellent problem-solving, debugging, communication skills and ownership mindset — where selected candidate takes responsibility for UI, APIs, and data pipelines from start to finish

• Experience with predictive intelligence, anomaly detection, or IoT data systems, familiarity with cloud-native design (Azure or multi-cloud) and/or contributions to open-source projects/ prior experience in start-up like environment, is preferred

Ability to understand Data Centre Mechanical & Electrical domain independently and contribute towards the product development, would be helpful to the role.