Product Platform Engineer

Skills –Apache Spark, Apache Flink, Apache Kafka / Amazon MSK, AWS and Azure cloud platforms, distributed data processing, data architecture, Docker, cloud storage (AWS S3 / Azure Blob), NoSQL databases, microservices, REST APIs, event-driven architecture, observability

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.

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 policiesand 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.

Engineering Practices:

• 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.

Preferred / Desirable Skills:

• Kubernetes, Amazon ECS/EKS, or Azure Kubernetes Service experience.
• Azure Event Hubs, Azure Functions, Azure Data Factory, or Azure Databricks exposure.
• 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.