Job Description

Roles & Responsibilities

As part of strengthening our Data teams, we are looking for profiles capable of designing, industrializing and optimizing data platforms (batch & real-time) within distributed environments based on Cloudera.

Your responsibilities:

Development & Industrialization

• Develop massive data processing pipelines in PySpark (Batch and Real-Time / Streaming modes).
• Set up real-time streams via Kafka (topics, partitions, schemes, offsets) and ensure ingestion with NiFi.
• Model and optimize NoSQL schemas, especially on Cassandra (tables, keys, clustering, replication) and Hive.
• Integrate and transform data from multiple sources (APIs, databases, streams, files).

Quality, Performance & Reliability

• Deploy Data Quality mechanisms (controls, monitoring, alerting).
• Optimize Spark processing (partitioning, tuning, data formats) specifically for distributed architectures.
• To ensure the supervision and resolution of incidents in a production environment.

CI/CD & Governance

• Industrialize developments via CI/CD chains (automated testing, deployments).
• Documenting flows, models and best practices.
• Collaborating occasionally with third-party teams on Dataviz topics and collaborating with Data Scientists (particularly on customer segmentation algorithms).
• Contribute to data governance (catalogue, traceability, security).

Desired Candidate Profile

Qualifications

Experience: ~4 years of experience in distributed environments and Big Data architectures.

Architecture: Strong interest and significant experience in On-Premise infrastructures.

Spark / PySpark: Essential mastery in Batch and Streaming processing.

NoSQL: Confirmed experience on Cassandra and/or Hive

Streaming & Ingestion: Very good mastery of Kafka and NiFi

Nice-to-have / Bonus:

• Good understanding of the Hadoop/HDFS ecosystem. Mastery of Cloudera is a plus but remains optional.
• Concepts in Data Science (models of customer clustering/segmentation) and Dataviz tools.
• Tools & DevOps: Skills in Git, CI/CD (GitLab CI) and an orchestration tool (Airflow, Luigi, Prefect).