Data & Automation Engineer Internship, Pigment

• Academic Background
• Currently enrolled in 4th year of engineering curriculum
• Strong academic foundation in Software Engineering, Data Engineering, or related technical field
• 4-month internship
• Solid programming skills in Python (experience with libraries for automation and data processing is a plus)
• Good understanding of SQL and database concepts
• Familiarity with version control systems (Git)
• Basic knowledge of cloud platforms (GCP experience is a plus but not required)
• Understanding of API integrations and RESTful services
• Strong problem-solving mindset and analytical thinking
• Excellent attention to detail and some understanding of what clean, maintainable code stands for
• Good communication skills in French and English (both written and verbal)
• Eagerness to learn new technologies and best practices
• Comfortable working in a collaborative, fast-paced environment

Desirable

• The Data & Automations team is designing, implementing, and maintaining intelligent automation solutions that streamline business processes and enhance data-driven decision making
• We empower business and corporate functions through technology integration, robust data architecture, and process optimization, enabling teams to focus on high-value activities while ensuring accuracy and scalability
• Our team operates across multiple domains, maintaining shared services and supporting Pigment’s internal functions through automation solutions
• We work with a variety of technologies to build reliable data pipelines and automation workflows that support Pigment's growth and operational efficiency
• This internship aims at working on several key projects relating to improving code quality, documentation, and developer experience for the Data & Automations team's internal tools and practices
• The internship will be organized in phases, that progress from revisiting one of the key internal development library we rely on, better integrating it with the rest of our modern data stack, to documentation and typing work, to more complex infrastructure and orchestration tasks
• This role involves collaborating with stakeholders within different teams at Pigment and requires strong communication skills, in both French and English
• Our core stack relies on Google Cloud Platform (GCP) and Python, with components including BigQuery, Cloud Run, Cloud Composer (Airflow), as well as dbt, Fivetran, Hightouch
• You will have the opportunity to learn and grow your skills across these technologies while making a meaningful impact on the Pigment’s operational efficiency
• The internship is structured in three progressive phases, moving from foundational improvements to more complex data infrastructure work:
• Phase 1: Onboarding & Peacock Improvements
• Focus: Transform Peacock into a core asset of our data engineering toolbox by improving typing, documentation, and refactoring, while enabling its direct usage within DAG components
• Key projects 1:
• Type Peacock Objects: Add Python type hints to improve IDE support and catch bugs early
• Document Peacock Package: Create comprehensive documentation and usage examples
• Enable DAG Integration: Refactor Peacock to support direct usage from Airflow DAG components, eliminating the need for wrapper services
• Skills learned: Python type hints, library design, cross-service dependencies, technical writing, documentation best practices, Airflow integration patterns
• Phase 2: Code Quality & Standards
• Focus: Implement automated CI agents, enhance linting standards, and create unified frameworks
• Key Projects 2:
• Add Automated CI Agents: Build agents for PII detection, anonymization checks, and other data related agents
• Review Linting: Enhance SQLFluff, Yamllint, and Ruff configurations across repositories
• Shared Utils Documentation: Audit and document duplicated utility functions
• Unified Automation Framework: Design standardized framework for automation services
• Skills learned: SQL best practices, code linting, dbt conventions, code architecture, refactoring strategies, system design trade-offs, software architecture, design patterns, framework design, testing strategies
• Phase 3: Data Synchronization Projects
• Focus: Implement data synchronization projects from the team backlog
• Approach:
• Start with easy single-direction syncs to build confidence
• Progress to medium complexity bi-directional syncs
• Follow established patterns: source system → BigQuery → destination system
• Skills learned: API integration, data mapping, ETL patterns, Airflow DAG development