Descrizione:
About Us: Amps Tek – a global technology leader since 2013 – is transforming how businesses approach technology and staffing solutions. Founded by seasoned technology leaders across the UK, Europe, APAC, North America, and LATAM, and with registered offices in 30+ countries, we deliver exceptional service, scalable solutions, and measurable impact. With a portfolio of 200+ clients and millions of users across web and mobile platforms, we empower businesses to innovate, grow, and succeed. Join our team and be part of a dynamic, growth-oriented organization that values talent, creativity, and results. Job Title: Confluent Solution Architect Location : Milan Italy Hybrid Role Type: B2 B Contract Description: As a Confluent Solution Architect, you will be responsible for designing, developing, and maintaining scalable real-time data pipelines and integrations using Kafka and Confluent components. You will collaborate with data engineers, architects, and Dev Ops teams to deliver robust streaming solutions.
5+ years of hands-on experience with Apache Kafka (any distribution: open-source, Confluent, Cloudera, AWS MSK, etc.) Strong proficiency in Java, Python, or Scala Solid understanding of event-driven architecture and data streaming patterns Experience deploying Kafka on cloud platforms such as AWS, GCP, or Azure Familiarity with Docker, Kubernetes, and CI/CD pipelines Excellent problem-solving and communication abilities Experience with Kafka Connect, Kafka Streams, KSQL, Schema Registry, REST Proxy, Confluent Control Center Hands-on with Confluent Cloud services, including ksql DB Cloud and Apache Flink Familiarity with Stream Governance, Data Lineage, Stream Catalog, Audit Logs, RBAC Confluent certifications (Developer, Administrator, or Flink Developer) Experience with Confluent Platform, Confluent Cloud managed services, multi-cloud deployments, and Confluent for Kubernetes Knowledge of data mesh architectures, KRaft migration, and modern event streaming patterns Exposure to monitoring tools (Prometheus, Grafana, Splunk) Experience with data lakes, data warehouses, or big data ecosystems