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.