Product Platform Engineer

Experion Technologies; Ernakulam; On-site; INR 3,000,000 - 5,200,000; Full time

Job summary

Experion Technologies Pvt. Ltd. is seeking a seasoned Product Platform Engineer to design and operate a cloud-native platform spanning AWS and Azure.

You will build streaming pipelines using Spark, Flink, and Kafka, implement reliable data flows, and containerize services for scalable deployment. The role emphasizes Medallion data layers, NoSQL data modelling, event-driven architecture, and integration of AI/GenAI services while maintaining robust security, observability, and production

Qualifications

• 4-7 years of professional software engineering experience.
• Hands-on experience with Apache Spark and Apache Flink for distributed batch and real-time stream processing.
• Strong understanding of Apache Kafka / Amazon MSK, including topics, partitions, producers, consumers, consumer groups, retries and failure handling.
• Practical exposure to AWS and Azure cloud services, including AWS S3, Azure Blob Storage and cloud-based data integration.
• Understanding of data architecture patterns, including Medallion Architecture (Bronze, Silver and Gold layers).
• Experience with NoSQL databases and analytical data modelling concepts, including star-schema modelling.
• Understanding of event-driven architectures, asynchronous communication, API design and distributed systems.
• To adhere to ISMS policies and procedures

Responsibilities

• Design, develop, and maintain cloud-native platform components across AWS and Azure.
• Build event-driven ingestion and processing pipelines using MSK/Kafka, Flink, Spark, or AWS Glue.
• Implement reliable data movement across streaming, processing, and cloud storage layers such as S3 and Azure Blob Storage.
• Containerize services using Docker and support deployment into managed container platforms.
• Design microservices with clear boundaries, asynchronous communication, resilience, security, observability, and production supportability.
• Apply Medallion Architecture patterns across bronze, silver, and gold data layers.
• Use NoSQL databases appropriately and support analytical models where starschema patterns are required.
• Integrate AI/ML or GenAI services into enterprise applications through secure, governed, and observable interfaces.
• Collaborate with application, data, infrastructure, security, and AI/ML teams to deliver reusable platform capabilities.
• API-first and event-driven design, including idempotency, retries, dead-letter handling, and replayability.
• Infrastructure as Code and automated CI/CD practices; Terraform, CloudFormation, Bicep, or equivalent exposure is desirable.
• Cloud security fundamentals: identity and access management, secrets management, encryption, network controls, and least privilege.
• Observability through structured logging, metrics, tracing, dashboards, and alerting.
• Unit, integration, contract, and performance testing for cloud and distributed workloads.
• Clear technical documentation, architecture diagrams, operational runbooks, and design‑decision records.
• AWS Lambda, ECS/Fargate, IAM, CloudWatch, Step Functions, and API Gateway exposure.
• Experience with schema management, data quality, lineage, metadata, governance, or catalog services.
• Understanding of distributed‑system trade-offs, performance tuning, cost optimization, and production incident troubleshooting

Skills

Apache Spark

Apache Flink

Apache Kafka / MSK

AWS

Azure

NoSQL databases

Event-driven architectures

Distributed systems

Data architecture patterns

AI/ML service integration

Observability

CI/CD

Tools

Docker

Kubernetes

Terraform

CloudFormation

Bicep

AWS Glue

S3

Azure Blob Storage

ECS/Fargate

CloudWatch

Job description

We are seeking an experienced Product Platform Engineer who build and operate scalable cloud-native platform and data-processing capabilities across AWS and Azure, with strong hands‑on engineering expertise in streaming, distributed processing, storage, containerization, microservices, data architecture, and AI/ML service integration.

Job Description

• 4-7 years of professional software engineering experience
• Hands-on experience with Apache Spark and Apache Flink for distributed batch and real-time stream processing.
• Strong understanding of Apache Kafka / Amazon MSK, including topics, partitions, producers, consumers, consumer groups, retries and failure handling.
• Practical exposure to AWS and Azure cloud services, including AWS S3, Azure Blob Storage and cloud-based data integration.
• Understanding of data architecture patterns, including Medallion Architecture (Bronze, Silver and Gold layers).
• Experience with NoSQL databases and analytical data modelling concepts, including star-schema modelling.
• Understanding of event-driven architectures, asynchronous communication, API design and distributed systems.
• To adhere to ISMS policies and procedures

Duties and Responsibilities

As a Product Platform Engineer, you will:

• Design, develop, and maintain cloud-native platform components across AWS and Azure.
• Build event-driven ingestion and processing pipelines using MSK/Kafka, Flink, Spark, or AWS Glue.
• Implement reliable data movement across streaming, processing, and cloud storage layers such as S3 and Azure Blob Storage.
• Containerize services using Docker and support deployment into managed container platforms.
• Design microservices with clear boundaries, asynchronous communication, resilience, security, observability, and production supportability.
• Apply Medallion Architecture patterns across bronze, silver, and gold data layers.
• Use NoSQL databases appropriately and support analytical models where starschema patterns are required.
• To adhere to ISMS policies and procedures
• Integrate AI/ML or GenAI services into enterprise applications through secure, governed, and observable interfaces.
• Collaborate with application, data, infrastructure, security, and AI/ML teams to deliver reusable platform
  capabilities.
• API-first and event-driven design, including idempotency, retries, dead-letter handling, and replayability.
• Infrastructure as Code and automated CI/CD practices; Terraform, CloudFormation, Bicep, or equivalent exposure is desirable.
• Cloud security fundamentals: identity and access management, secrets management, encryption, network controls, and least privilege.
• Observability through structured logging, metrics, tracing, dashboards, and alerting.
• Unit, integration, contract, and performance testing for cloud and distributed workloads.
• Clear technical documentation, architecture diagrams, operational runbooks, and design‑decision records.
• AWS Lambda, ECS/Fargate, IAM, CloudWatch, Step Functions, and API Gateway exposure.
• Experience with schema management, data quality, lineage, metadata, governance, or catalog services.
• Understanding of distributed‑system trade-offs, performance tuning, cost optimization, and production incident troubleshooting