Job Title: Engineer - Data Platform Full Stack

Location: Bangalore, India

Years of Experience: 5 - 8 Years

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

Reporting To: Engineering Manager

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 architectures. 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 up-to-date 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.
- Solid 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 modern 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.

Ideal Candidate Profile The ideal candidate is a hands-on Platform Engineer with solid AWS, Full Stack Development, and Platform Engineering expertise who can design and operate enterprise-scale Data & AI platforms. They should possess a strong foundation in cloud-native engineering, microservices, DevOps, data platform technologies, and AI enablement capabilities. Experience supporting modern Data, Analytics, AI, and Agentic AI ecosystems within large enterprise environments is highly desirable.

Ways of Working

- Hands-on engineering and platform delivery role.
- Build reusable platform services and engineering accelerators.
- Enable Data Engineering and Analytics as a Service capabilities.
- Collaborate closely with Data, AI, and Business teams.
- Drive platform modernization, automation, and innovation initiatives.
- Support enterprise Data & AI transformation programs.

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