Data Streaming Engineer Apache Flink (Pune)

We are looking for an experienced Data Streaming Engineer with solid hands-on expertise in Apache Flink and Apache Spark to design, develop, and optimize large-scale real-time and batch data processing solutions.

The candidate should have strong experience in Flink DataStream API, Flink Table API/SQL, Spark Core, Spark SQL, and Spark Structured Streaming, with a solid understanding of distributed stream processing and high-volume data pipelines.

Mandatory Skills

• 810 years of experience in Data Engineering / Big Data / Streaming.
• Strong hands-on experience with Apache Flink.
• Expertise in Flink DataStream API.
• Strong knowledge of Flink Table API & Flink SQL API.
• Experience with stateful stream processing, event-time processing, windowing, watermarks, and late-event handling.
• Strong understanding of Flink checkpointing, fault tolerance, and distributed deployment.
• Strong experience with Apache Spark.
• Hands-on expertise in Spark Core, Spark SQL, and Spark Structured Streaming.
• Experience developing high-volume ETL/data transformation pipelines.
• Strong understanding of Spark execution architecture, resource management, performance tuning, and optimization.
• Strong understanding of distributed systems and real-time data processing.
• Proficiency in at least one programming language: Java / Scala / Python.

Good to Have

• Apache Kafka / Azure Event Hubs or other messaging platforms.
• Azure / AWS / GCP cloud experience.
• Docker / Kubernetes.
• CI/CD and DevOps practices.
• Experience with Data Lakes / Lakehouse architectures.
• Microservices and event-driven architecture.
• Monitoring, logging, troubleshooting, and data quality.

Key Responsibilities

• Design and develop scalable real-time streaming pipelines using Flink and Spark.
• Build event-driven and near-real-time data processing solutions.
• Develop batch and streaming ETL workflows.
• Optimize pipelines for low latency, high throughput, reliability, and scalability.
• Implement Flink stat .