Data Engineer – Data Platform Engineering
We are looking for a highly skilled Data Engineer to design, build, and scale a modern cloud-native Data Platform supporting batch, streaming, analytics, and real-time data use cases across the organization.
The ideal candidate should have solid expertise in distributed data systems, platform engineering, cloud infrastructure, and building reliable, scalable, and observable data ecosystems.
Key Responsibilities
Design and develop scalable enterprise data platform architecture on AWS Build reusable frameworks for data ingestion, transformation, orchestration, and consumption Develop batch and real-time data pipelines using distributed processing technologies Build and maintain streaming platforms for event-driven architectures Create standardized data lake/lakehouse foundations for analytics and downstream applications Implement metadata management, schema evolution, lineage, governance, and access control Develop platform capabilities for data reliability, observability, monitoring,



and alerting Enable self-service data access and scalable data consumption layers Build CDC and incremental ingestion frameworks from databases and external systems Optimize platform scalability, performance, fault tolerance, and cloud cost Automate deployments, infrastructure provisioning, and CI/CD for data workloads Collaborate with Analytics, Product, and Engineering teams to onboard new data domains Ensure platform security, compliance, and operational excellence Technical Skills
Core Technologies
Strong expertise in: Apache Spark Spark Streaming Apache Flink SQL Python Distributed data processing systems Streaming & Event Platforms
Hands-on experience with: Apache Kafka Kafka Connect / Kafka Streams Real-time event-driven architectures AWS Data Platform
Strong experience with: Amazon Web Services AWS Glue AWS Athena AWS DMS Amazon S3 Amazon Cloud Watch Understanding of IAM, networking, security, and cloud-native architecture Data Platform

📌 Data engineer (Pune)
🏢 SPIRO
📍 Pune