Job Title: Data Engineer - Ingestion, Storage & Streaming (Confluent Kafka) Job Summary: As a Data Engineer specializing in Ingestion, Storage, and Streaming, you will design, implement, and maintain robust, scalable, and high-performance data pipelines for the efficient flow of data through our systems. You will work with Confluent Kafka to build real-time data streaming platforms, ensuring high availability and fault tolerance. You will also ensure that data is ingested, stored, and processed efficiently and in real-time to provide immediate insights. Key Responsibilities: Kafka-Based Streaming Solutions: Design, implement, and manage scalable and fault-tolerant data streaming platforms using Confluent Kafka. Develop real-time data streaming applications to support business-critical processes. Implement Kafka producers and consumers for ingesting data from various sources. Handle message brokering, processing, and event streaming within the platform. Ingestion & Data Integration: Build efficient data ingestion pipelines to bring real-time and batch data from various data sources into Kafka. Ensure smooth data integration across Kafka topics and handle multi-source data feeds. Develop and optimize connectors for data ingestion from diverse systems (e.g., databases, external APIs, cloud storage). Data Storage and Management: Manage and optimize data storage solutions in conjunction with Kafka, including topics, partitions, retention policies, and data compression. Work with distributed storage technologies to store large volumes of structured and unstructured data, ensuring accessibility and compliance. Implement strategies for schema management, data versioning, and data governance. Data Streaming & Processing: Leverage Kafka Streams and other stream processing frameworks (e.g., Apache Flink, ksqlDB) to process real-time data and provide immediate analytics. Build and optimize data processing pipelines to transform, filter, aggregate, and enrich streaming data. Monitoring, Optimization, and Security: Set up and manage monitoring tools to track the performance of Kafka clusters, ingestion, and streaming pipelines. Troubleshoot and resolve issues related to data flows, latency, and failures. Ensure data security and compliance by enforcing appropriate data access policies and encryption techniques. Collaboration and Documentation: Collaborate with data scientists, analysts, and other engineers to align data systems with business objectives. Document streaming architecture, pipeline workflows, and data governance processes to ensure system reliability and scalability. Provide regular updates on streaming and data ingestion pipeline performance and improvements to stakeholders. Required Skills & Qualifications: Experience: 3+ years of experience in data engineering, with a strong focus on Kafka, data streaming, ingestion, and storage solutions. Hands-on experience with Confluent Kafka, Kafka Streams, and related Kafka ecosystem tools. Experience with stream processing and real-time analytics frameworks (e.g., ksqlDB, Apache Flink). Technical Skills: Expertise in Kafka Connect, Kafka Streams, and Kafka producer/consumer APIs. Proficient in data ingestion and integration techniques from diverse sources (databases, APIs, etc.). Strong knowledge of cloud data storage and distributed systems. Experience with programming languages like Java, Scala, or Python for Kafka integration and stream processing. Familiarity with tools such as Apache Spark, Flink, Hadoop, or other data processing frameworks. Experience with containerization and orchestration tools such as Docker, Kubernetes.
