Job description

Responsibilities Design data pipelines: Build robust, scalable, and secure data pipelines to ingest, process, and move data from various sources into the data lake using Kafka. Administer Kafka clusters: Deploy, configure, and maintain Kafka clusters and related ecosystem tools, such as Kafka Connect and Schema Registry, ensuring high availability and performance. Manage the data lake: Oversee the architecture and governance of the data lake, including managing data storage (e.g., in AWS S3 or ADLS), security, and metadata. Develop data processing applications: Create producers and consumers to interact with Kafka topics using programming languages like Python, Java, or Scala. Perform stream processing: Use tools like Kafka Streams, Apache Flink, or ksqlDB to perform real-time data transformations and analytics. Ensure data quality and security: Implement data quality checks, manage data lineage, and enforce security controls such as encryption, access controls (ACLs), and compliance (e.g., GDPR). Monitor and troubleshoot: Set up monitoring and alerting for Kafka and data lake infrastructure and respond to incidents to ensure operational reliability. Collaborate with teams: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver reliable data solutions. Essential skills and qualifications Experience: Proven experience designing and managing data platforms with Apache Kafka and big data technologies. Programming: Strong proficiency in languages like Python, Java, or Scala. Big data technologies: Expertise in big data processing frameworks, such as Apache Spark and Apache Flink. Cloud platforms: Hands-on experience with cloud environments (AWS, Azure, or GCP) and relevant services like S3, Glue, or Azure Data Lake Storage. Data lake architecture: A solid understanding of data lake design principles, including storage formats (e.g., Delta Lake, Apache Iceberg), data modeling, and governance. Databases: Experience with various database systems, including both SQL and NoSQL. Infrastructure management: Familiarity with infrastructure-as-code tools like Terraform or Ansible and containerization with Docker and Kubernetes. Professionals in this field can advance from entry-level data engineering positions to senior roles, and then to a Big Data Architect or Solutions Architect, where they oversee large-scale data infrastructure. Education: Bachelor's Degree - Degree in engineering, or a related scientific or technical discipline is required. Substitution for Education - 10 years or 8 years of additional relevant experience may be substituted for education Relevant certifications Pursuing certifications can validate your expertise and boost your career. For Kafka: Confluent Certified Administrator for Apache Kafka (CCAAK) Confluent Certified Developer for Apache Kafka (CCDAK) For Data Lake and Cloud: Databricks Certified Data Engineer AWS Certified Data Engineer Microsoft Certified: Azure Data Engineer Associate

Requirements