Confluent Consulting Engineer (Contract – Germany, Remote)

? Location: Germany (Mostly Remote, occasional client travel)

? Duration: 6–12 months (extension possible)

? Work Mode: Mostly Remote

? Experience: 5+ years relevant / 10+ years total

About the Role

We are looking for a Confluent Consulting Engineer to design, build, and maintain real-time data streaming solutions using Apache Kafka and the Confluent ecosystem. You will collaborate with data engineering, architecture, and DevOps teams to implement enterprise-grade streaming pipelines that support mission-critical applications.

Key Responsibilities

Design and implement real-time data pipelines using Kafka and Confluent components

Build integrations and streaming solutions across various data systems

Support cloud deployments (AWS, GCP, Azure) of Kafka clusters

Collaborate with architects, data engineers, and DevOps teams

Troubleshoot, optimize performance, and ensure high availability

Contribute to best practices, documentation, and architectural guidance

Must-Have Skills

5+ years hands-on Apache Kafka experience (Confluent, Open Source, Cloudera, MSK, etc.)

Strong proficiency in Java, Python, or Scala

Solid understanding of event-driven architecture and streaming patterns

Experience with Kafka deployments on AWS, Azure, or GCP

Familiarity with Docker, Kubernetes, and CI/CD pipelines

Excellent problem-solving and communication skills

German + English proficiency

Preferred Qualifications

Candidates with Confluent ecosystem experience will be prioritized:

Kafka Connect, Kafka Streams, KSQL, Schema Registry, REST Proxy, Control Center

Confluent Cloud services (ksqlDB Cloud, Apache Flink)

Knowledge of Stream Governance, Stream Catalog, Data Lineage, Audit Logs, RBAC

Confluent certifications (Developer, Administrator, Flink Developer)

Experience with Confluent Platform, Confluent for Kubernetes, multi-cloud deployments

Familiarity with Data Mesh, KRaft migration, and modern streaming architectures

Experience with monitoring tools: Prometheus, Grafana, Splunk

Background in data lakes, data warehouses, or big data ecosystems

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