Data Platform Engineer - Full Stack
Vriba
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Job summary
Vriba is seeking an experienced Platform Engineer to design, build, and operate enterprise-grade Data & AI Platform capabilities in a cloud-first environment. The role emphasizes scalable, governed, and self-service data consumption with AI-driven workflows.
The candidate will contribute to Data Engineering as a Service, Self-Service Analytics, and AI-ready architectures, requiring hands-on work across AWS Cloud, Full Stack, microservices, and DevOps practices.
Qualifications
- Bachelor's degree in Engineering required.
- 5-8 years of hands-on experience in data/platform engineering.
- Strong experience with AWS, full-stack development, and platform modernization.
- Experience building enterprise-scale data and analytics platforms.
Responsibilities
- Design, develop, and operate enterprise Data & AI Platform capabilities.
- Build Data Engineering as a Service, Data Quality as a Service, and governance features.
- Develop Semantic Layer and Self-Service Analytics for business users and AI apps.
- Enable trusted data access and AI-ready platform architectures.
- Create cloud-native apps, APIs, and platform services with reusable accelerators.
- Implement CI/CD, IaC, and cloud-native operational frameworks.
- Collaborate with Data Engineers, Data Scientists, and stakeholders.
- Support AI-ready platform modernization and cost optimization initiatives.
Skills
Education
Tools
Job description
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
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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)
- CI/CD Pipelines
- Infrastructure as Code (Terraform, CloudFormation, CDK)
- Git
- Docker
- Kubernetes
- Platform Monitoring & Observability
- Logging & Operational Tooling
- DevOps Practices
- 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
- 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
- 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
- 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.
- Bachelor's Degree in Engineering (Mandatory)
- Master's Degree in Computer Science or IT (Preferred)
- 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 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.
The ideal candidate is a hands-on Platform Engineer with strong 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.
- 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.
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