Data Engineer

As the Senior Data Engineer, you will be responsible for owning the end-to-end technical design and architecture of batch and streaming data pipelines. Your key responsibilities will include: - Leading the development of high-throughput, low-latency data processing systems using Java - Designing and implementing real-time streaming solutions such as event ingestion, processing, and aggregation - Driving code quality, performance optimization, and scalability across systems - Reviewing code, mentoring engineers, and setting engineering best practices - Collaborating with product, platform, infra, and security teams to deliver production-grade solutions - Ensuring data reliability, fault tolerance, and observability through metrics, logs, and alerts - Automating operational workflows and deployment pipelines - Participating in capacity planning, cost optimization, and system tuning - Staying current with evolving Big Data and streaming ecosystems and evaluating new technologies Qualifications required for this role include: - Strong proficiency in Java with a focus on multithreading, concurrency, and JVM tuning - Hands-on experience with distributed data processing frameworks such as Apache Spark (Core, SQL, Streaming / Structured Streaming / Apache Flink) and Apache Kafka (producers, consumers, partitions, offsets, exactly-once semantics) - Solid understanding of batch + streaming architectures (Lambda / Kappa patterns) - Experience with Hadoop ecosystem components like HDFS, Hive, YARN or equivalents - Strong knowledge of relational databases (PostgreSQL, MySQL) and experience with NoSQL / distributed datastores (HBase, Cassandra, MongoDB, Pinot etc.) - Understanding of data modeling, partitioning, and schema evolution - Experience with Linux-based systems, production deployments, containerization, orchestration, monitoring tools, CI/CD pipelines, and automated testing frameworks In addition to the required qualifications, the following skills are considered a plus: - Experience with Flink / Kafka Streams / real-time analytics - Exposure to cloud-native data platforms (AWS, GCP, Azure) - Knowledge of data governance, security, and access control - Experience in telecom, fintech, or large-scale consumer data platforms This role offers the opportunity to work on large-scale, real-time data systems with high technical ownership and architectural influence. You will also have the chance to shape next-generation streaming and analytics platforms. As the Senior Data Engineer, you will be responsible for owning the end-to-end technical design and architecture of batch and streaming data pipelines. Your key responsibilities will include: - Leading the development of high-throughput, low-latency data processing systems using Java - Designing and implementing real-time streaming solutions such as event ingestion, processing, and aggregation - Driving code quality, performance optimization, and scalability across systems - Reviewing code, mentoring engineers, and setting engineering best practices - Collaborating with product, platform, infra, and security teams to deliver production-grade solutions - Ensuring data reliability, fault tolerance, and observability through metrics, logs, and alerts - Automating operational workflows and deployment pipelines - Participating in capacity planning, cost optimization, and system tuning - Staying current with evolving Big Data and streaming ecosystems and evaluating new technologies Qualifications required for this role include: - Strong proficiency in Java with a focus on multithreading, concurrency, and JVM tuning - Hands-on experience with distributed data processing frameworks such as Apache Spark (Core, SQL, Streaming / Structured Streaming / Apache Flink) and Apache Kafka (producers, consumers, partitions, offsets, exactly-once semantics) - Solid understanding of batch + streaming architectures (Lambda / Kappa patterns) - Experience with Hadoop ecosystem components like HDFS, Hive, YARN or equivalents - Strong knowledge of relational databases (PostgreSQL, MySQL) and experience with NoSQL / distributed datastores (HBase, Cassandra, MongoDB, Pinot etc.) - Understanding of data modeling, partitioning, and schema evolution - Experience with Linux-based systems, production deployments, containerization, orchestration, monitoring tools, CI/CD pipelines, and automated testing frameworks In addition to the required qualifications, the following skills are considered a plus: - Experience with Flink / Kafka Streams / real-time analytics - Exposure to cloud-native data platforms (AWS, GCP, Azure) - Knowledge of data governance, security, and access control - Experience in telecom, fintech, or large-scale consumer data platforms This role offers the opportunity to work on large-scale, real-time data systems with high technical ownership and architectural influence. You will also have the chance to shape next-generation streaming and analytics platforms.