Senior Data Engineer

Description At Cognyte, you’ll collaborate with expert colleagues around the globe to solve problems most people will never even know exist! You’ll be part of building unique solutions shaped by real investigative methodologies, enabling our customers to identify, investigate, visualize and prevent criminal, terror and security threats worldwide. These solutions are used by law enforcement, national security, and national and military intelligence agencies in almost 100 countries to turn massive, diverse data into clear, actionable intelligence for a safer world. We are looking for a passionate Senior Data Engineer to join our Data Platform team. In this role, you will be an individual contributor working on the design and implementation of our data lakehouse infrastructure. You will help build scalable, high-performance data foundations that support analytics, BI, and future data-driven capabilities across the platform. The mission is to deliver a successfully working data lakehouse based on modern data engineering technologies, medallion architecture, scalable processing, and reliable data quality. As a Cognyter you will: Design, build, and optimize scalable data lakehouse platforms using technologies such as Spark, Iceberg, Trino, ClickHouse, and AWS/hybrid infrastructure. Develop and maintain batch and streaming data pipelines, workflow orchestration, and data-serving layers, ensuring reliability, scalability, and performance. Own data modelling, medallion architecture (bronze, silver, gold), ClickHouse optimization, cost estimation, and platform governance, including data quality, lineage, and observability. Collaborate with BI users, engineers, and stakeholders to deliver curated datasets, support analytics and reporting, and contribute to architectural decisions. Provide hands-on support for platform operations, infrastructure-related tasks, and continuous improvement of the data ecosystem. Requirements For that mission you'll need: 5+ years in software/data engineering, including 3+ years in hands-on data engineering and experience in a senior engineering role. Strong expertise in SQL, Python, Apache Spark, and ClickHouse (schema design, materialized views, performance tuning, scalable query optimization). Experience with Kubernetes and building, operating, and optimizing production-grade data pipelines. Solid understanding of data warehousing, data lakes/lakehouses, data modeling, data quality, scalability, and performance optimization. Experience working with large-scale distributed systems, high-volume data, and AWS and/or hybrid cloud environments. Hands-on experience with several of the following: Apache Iceberg, Nessie, AWS Glue, Argo Workflows, PostgreSQL, Elasticsearch, Neo4j, Helm, Jenkins, and Kubernetes. Good English communication skills. Nice to have: Experience with Apache Iceberg, Apache Ranger, data governance, access control, and data security. Experience with BI platforms, particularly Apache Superset. Experience building data platforms from scratch and optimizing cloud or hybrid data infrastructure.