Senior Data Engineer - CDD

Context

The Europe Data Team at CMA CGM is responsible for delivering end-to-end data platforms, data products, and analytics capabilities for the European Regional Office. Working closely with business stakeholders, IT teams, and Head Office, the team delivers secure, scalable, and business-driven data solutions.

In the European region, delivery often occurs in a transitional environment where global data platforms or products are not yet fully deployed. As a result, regional data products and intermediate solutions are developed to meet immediate business needs while ensuring alignment with future enterprise platforms.

Within this context, Data Engineers must combine strong data platform expertise, delivery pragmatism, and business orientation. Solutions must be:

• Delivered rapidly

• Designed for scalability and reuse

• Aligned with enterprise data architecture (Snowflake, governance models)

• Built with maintainability and future integration in mind

The Europe Data Team is currently accelerating the development of regional data products, data pipelines, and analytics platforms to support business visibility, KPI tracking, and operational decision-making. We are therefore looking for a hands-on Data Engineer to design, build, and maintain scalable data solutions supporting these initiatives.

Missions

As a Data Engineer, you will:

Data Architecture & Platform

• Design and implement scalable data architectures and multi-layer platforms (raw, curated, consumption layers)

• Ensure alignment with CMA CGM global data strategy

Data Pipelines & Transformation

• Design, develop, and optimise ETL/ELT pipelines

• Implement reusable and scalable transformation logic

Data Modelling

• Build and maintain dimensional data models (star schema, data marts)

• Standardise KPI-ready datasets

Data Quality & Governance

• Ensure data quality, integrity, and reliability

• Apply governance, security, and RBAC controls

Orchestration & Standards

• Use and enforce standards with Airflow and dbt

• Contribute to engineering best practices

Collaboration & Delivery

• Work with Business Analysts, Product Owners, and stakeholders

• Deliver solutions aligned with business value and timelines

Performance & Optimisation

• Optimise pipelines and queries (especially on Snowflake)

• Improve performance and cost efficiency

Documentation & Knowledge Sharing

• Produce concise, delivery-focused documentation

• Support knowledge transfer

Continuous Improvement

• Contribute to platform evolution and innovation initiatives

Tools & Environment

• Snowflake

• AWS

• dbt

• Apache Airflow

• JIRA

• Confluence

• Lucid

Expected Deliverables

1. Data Architecture & Design Deliverables

   • Data Architecture Diagram (End-to-End flow: Source → Raw → Curated → Consumption)

   • Data Layer Design (Bronze / Silver / Gold or Raw / Curated / Data Mart)

   • Data Mapping Specification (Source → Target transformation logic)

   • Data Product Technical Design Document

   • Data Model Design (Star schema / dimensional models)

2. Data Pipeline & Engineering Deliverables

   • ETL / ELT Pipelines (Production-ready)

   • Data Ingestion Framework (batch / incremental loads)

   • Transformation Logic (SQL / dbt models)

   • Orchestration Workflows (Airflow DAGs)

   • Pipeline Monitoring & Logging setup

   • Error handling and retry logic implementation

3. Data Modelling & Consumption Layer

   • Snowflake Data Models (fact / dimension tables)

   • Certified Data Mart(s) ready for reporting

   • KPI-ready datasets (governed tables)

   • Aggregated / optimised reporting layers

   • Data Dictionary (technical + business mapping)

4. Data Quality & Observability Deliverables

   • Data Quality Rules (business + technical)

   • Data Validation Scripts (completeness, freshness, accuracy)

   • Monitoring Dashboards (SLA, pipeline health)

   • Data Quality Incident Logs / Tracking

   • Observability Configuration (alerts, anomaly detection)

5. Governance & Compliance Deliverables

   • Data Access Model (RBAC definitions)

   • Security & Compliance Documentation

   • Data Lineage Documentation (source-to-consumption)

   • Naming conventions & development standards

   • Data Governance alignment artefacts (for GATE process)

6. Performance & Optimisation Deliverables

   • Query optimisation scripts (Snowflake tuning)

   • Cost optimisation recommendations (compute/storage)

   • Data pipeline performance benchmarks

   • Partitioning / clustering strategies documentation

7. Integration & Delivery Deliverables

   • Data interfaces / exposed datasets for:

     • Qlik dashboards

     • Power BI / external tools

     • APIs (if applicable)

   • Integration documentation (data contracts)

   • Release package for deployment (DEV → UAT → PROD)

   • Support for downstream teams (BI / Analytics)

8. Documentation & Knowledge Sharing

   • Technical Documentation (pipelines, models, architecture)

   • Runbook (operations & support)

   • Handover documentation

   • Onboarding & knowledge transfer sessions

9. Testing & Validation Deliverables

   • Unit Testing (pipeline & transformations)

   • Integration Testing (end-to-end data flow)

   • Data Reconciliation Reports

   • UAT Support & validation outputs

   • Production readiness checklist

10. Continuous Improvement & Backlog

    • Technical backlog (enhancements / fixes)

    • Automation improvements

    • Refactoring of pipelines / models

    • Recommendations for platform evolution

Working Conditions

• Reference: #44 Senior

• Profile for the mission: Senior Data Engineer

• Seniority: 8+ years of experience

• Location: Remote within a compatible European time zone; occasional onsite presence may be required for key workshops, project kick-offs, or alignment sessions

• Mandatory onsite work: No

• Start date: July 20th, 2026 / ASAP

1. 8+ years of experience in data engineering
2. Strong expertise in data engineering principles, data architecture, and large-scale data platforms
3. Advanced proficiency in SQL and data modelling (dimensional modelling, star schema)
4. Proven experience with ETL / ELT pipeline design and optimisation
5. Hands-on experience with Apache Airflow (orchestration), dbt (data transformation & modelling), and Snowflake (preferred data platform)
6. Experience with cloud environments such as Azure, AWS or equivalent
7. Strong knowledge of data governance frameworks, data lifecycle management, and security and access control (RBAC)
8. Familiarity with version control (Git), CI/CD pipelines, and Agile / Scrum methodologies
9. Strong ability to diagnose complex data issues and propose pragmatic solutions
10. Ability to communicate effectively with both technical and business stakeholders
11. Experience working in international and distributed teams
12. Ability to produce efficient, pragmatic documentation focused on delivery value