Job Title: Full Stack Data Platform Engineer

Location: Bangalore, India

Experience: 5 - 8+ Years

Function: Technology | Data Platform Engineering | Cloud & AI Platforms

Mode: Initially 2 weeks hybrid then work from home

Primary skills - Full stack (React, NodeJs, CI/CD, etc.)

Secondary skills - Data engineering (including some exposure to Snowflake, Databricks or similar data platform), AI/Agentic The resources are expected to build service layer that could be used by Data Engineers

Role Summary

Client is seeking a highly experienced Platform Engineer to design, build, and operate enterprise-grade Data & AI Platform capabilities supporting Analytics, AI, Generative AI, and Agentic AI workloads. The role is focused on modernizing platform capabilities that enable scalable, governed, and self-service data consumption while supporting AI-driven decision-making and autonomous workflow.

The successful candidate will contribute to Data Engineering as a Service, Self-Service Analytics, Semantic Layers, Intelligent Data Products, and AI-ready architecture. This is a hands-on engineering role requiring expertise in AWS Cloud, Platform Engineering, Full Stack Development, Microservices Architecture, DevOps, and modern software engineering practices.

Key Responsibilities

Data & AI Platform Engineering

- Design, develop, and enhance enterprise Data & AI Platform capabilities supporting analytics, AI, Agentic AI, and modern data platform use cases.
- Build platform capabilities enabling Data Engineering as a Service, Data Quality as a Service, Data Discovery, Governance, and standardized data consumption.
- Develop Semantic Layer and Self-Service Analytics capabilities for business users, analysts, and AI applications.
- Enable trusted and governed data access across enterprise platforms.
- Support intelligent data products and AI-ready platform architectures.

Application & Platform Development

- Build cloud-native applications, APIs, platform services, automation frameworks, and developer productivity tools.
- Develop reusable platform services, accelerators, SDKs, and engineering frameworks.
- Design capabilities supporting data ingestion, transformation, orchestration, observability, and platform operations.
- Build scalable and resilient enterprise applications using modern software engineering patterns.

Cloud & Platform Operations

- Develop secure, scalable, and resilient AWS-based platform solutions.
- Implement CI/CD, Infrastructure as Code, DevOps automation,



and cloud-native operational frameworks.
- Contribute to platform reliability, cloud cost optimization, performance tuning, incident management, and operational excellence initiatives.
- Support enterprise security, privacy, governance, and compliance requirements.

Data Governance & Enablement

- Contribute to metadata management, lineage, data quality, observability, governance, and compliance initiatives.
- Support semantic retrieval and integration capabilities for AI-driven workloads.
- Enable enterprise-wide self-service analytics and trusted data consumption frameworks.
- Drive enterprise data enablement through modern governance and metadata practices.

AI & Innovation

- Support AI-ready platform architecture and emerging AI technologies.
- Enable integrations supporting Agentic AI, Generative AI, intelligent automation, and orchestration platforms.
- Collaborate with data scientists and AI teams to build scalable AI-enablement capabilities.
- Contribute to modern AI engineering and emerging technology initiatives.

Stakeholder Collaboration

- Work closely with Data Engineers, Data Scientists, Platform Engineers, Architects, and business stakeholders.
- Deliver reusable services and platform capabilities that accelerate adoption and innovation.
- Translate business needs into scalable engineering solutions.
- Drive collaboration across business and technology teams.

Primary Skills (Must Have) Platform Engineering & Cloud

- AWS Cloud Platform
- ECS / EKS
- AWS Lambda
- API Gateway
- S3
- IAM
- VPC
- CloudWatch
- Secrets Manager
- Route 53
- Enterprise Platform Engineering
- Internal Developer Platforms
- Cloud Security & Governance
- Distributed Systems Architecture

Software Engineering

- React
- TypeScript
- JavaScript
- Node.js
- REST APIs
- GraphQL APIs
- Microservices Architecture
- Domain Driven Design (DDD)
- API First Development
- Service-Oriented Architecture
- Secure Coding Practices
- Testing Frameworks (Unit, Integration, Contract & End-to-End Testing)

DevOps & Automation

- CI/CD Pipelines
- Infrastructure as Code (Terraform, CloudFormation, CDK)
- Git
- Docker
- Kubernetes




- Platform Monitoring & Observability
- Logging & Operational Tooling
- DevOps Practices

Data & Analytics Platforms

- Snowflake
- Databricks
- AWS Data Services
- Kafka
- Data Warehousing
- Lakehouse Architecture
- Data Mesh
- Data Fabric
- Medallion Architecture
- Data Products
- Semantic Layers
- Self-Service Analytics
- Data Governance
- Data Quality
- Metadata Management
- Data Lineage

Secondary Skills (Nice to Have) AI & Emerging Technology

- Generative AI
- Agentic AI
- AI Engineering Practices
- LLM Integrations
- Vector Databases
- MCP Servers
- AI Orchestration Frameworks
- AI Governance
- Azure AI
- OpenAI
- Amazon Bedrock
- Snowflake Cortex

Additional Desirable Skills

- MLOps
- AI Platform Engineering
- Model Serving Platforms
- Event Driven Architectures
- Streaming Platforms
- Multi-Cloud Environments
- Airflow
- Astronomer
- Apache Spark
- Databricks
- GraphQL
- API Gateways
- Internal Developer Platforms
- Retail & eCommerce Industry Experience

Key Competencies

- Strong Platform Engineering mindset.
- Expertise in scalable enterprise-grade platform development.
- Strong understanding of Data Products and Semantic Layers.
- Knowledge of Data Governance and AI Governance frameworks.
- Focus on automation, standardization, and platform innovation.
- Strong analytical and problem-solving skills.
- Ability to operate in rapidly evolving cloud and AI environments.
- Excellent communication and stakeholder management capabilities.
- Ability to translate business requirements into scalable technical solutions.

Experience & Qualifications

Education

- Bachelor's Degree in Engineering (Mandatory)
- Master's Degree in Computer Science or IT (Preferred)

Experience

- 5-8 years of relevant experience.
- Hands-on experience building and operating enterprise-grade production platforms.
- Strong experience implementing cloud-native architectures and distributed systems.
- Proven expertise with AWS platform development.
- Experience building scalable Full Stack applications using React, TypeScript/JavaScript, and Node.js.
- Experience supporting Data & AI platform modernization programs.
- Experience implementing contemporary development practices including Agile, DevOps, Git, CI/CD, IaC, and automated testing.
- Experience supporting reliability, observability, governance, security, and cloud cost optimization initiatives.
- Retail, eCommerce, or customer-facing digital platform experience is highly preferred.

📌 Full Stack Data Platform Engineer (Bengaluru)
🏢 Vriba
📍 Bengaluru