Senior Data Engineer

Akkodis is revitalizing its data engineering practice and is searching for impact-driven Data Engineers to help build a modern data platform supporting analytics, AI, and machine learning at enterprise scale. You will join a collaborative, forward-thinking team building reliable data pipelines, curated datasets, and lakehouse-aligned architectures.

Responsibilities:

• Python & SQL: Production‑grade development for ETL/ELT, data transformations, and complex query optimization
• Cloud (Azure & AWS): Experience deploying data workloads, managing storage, security, and scalable cloud services
• Databricks / Spark: Building distributed data pipelines, optimizing performance, and working with Delta/Lakehouse features
• Lakehouse & Medallion Architecture: Designing curated Bronze/Silver/Gold layers and governed analytical datasets
• Iceberg Tables: Experience or familiarity with Iceberg, Delta, or Hudi for large‑scale ACID‑compliant data storage
• Kafka / Confluent: Streaming ingestion, event‑driven architectures, and high‑throughput real‑time pipelines
• Dagster & dbt: Orchestrating workflows and building modular, tested, documentation‑rich data models
• Kubernetes: Running containerized jobs/services, understanding deployment patterns and cluster operations
• Data Science / ML Exposure: Supporting feature engineering, ML pipelines, and model‑ready datasets
• CI/CD & Git: Automated deployments, branch strategy best practices, and quality‑driven code workflows
• AI‑Assisted Development (Preferred): Using AI tools to accelerate coding, testing, and documentation

Requirements:

• Production‑grade development for ETL/ELT, data transformations, and complex query optimization using Python & SQL
• Experience deploying data workloads, managing storage, security, and scalable cloud services in Cloud (Azure & AWS)
• Building distributed data pipelines, optimizing performance, and working with Delta/Lakehouse features using Databricks / Spark
• Designing curated Bronze/Silver/Gold layers and governed analytical datasets in Lakehouse & Medallion Architecture
• Experience or familiarity with Iceberg, Delta, or Hudi for large‑scale ACID‑compliant data storage
• Streaming ingestion, event‑driven architectures, and high‑throughput real‑time pipelines using Kafka / Confluent
• Orchestrating workflows and building modular, tested, documentation‑rich data models using Dagster & dbt
• Running containerized jobs/services, understanding deployment patterns and cluster operations in Kubernetes
• Supporting feature engineering, ML pipelines, and model‑ready datasets with Data Science / ML Exposure
• Automated deployments, branch strategy best practices, and quality‑driven code workflows using CI/CD & Git
• Using AI tools to accelerate coding, testing, and documentation in AI‑Assisted Development