In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and space, our architects design innovative solutions that make our tomorrow's possible.

JOB OBJECTIVE

As a Data Engineer, you will be participate in the different phases of Thales Digital Transformation Projects by collecting, modeling, and processing data on Google Cloud Platform (GCP) to enable end users to perform accurate analysis. Leveraging tools like BigQuery, Cloud Storage, Dataproc, and airflow, you’ll build scalable data pipelines, design robust data models, and support the creation of dashboards.

If you have experience with Talend, it will be considered a plus.

Roles and Responsibilities

Assemble large, complex datasets that meet both functional and non-functional business requirementsIntegrate business data from diverse systems to build a unified, analytics-ready data foundationSupport project qualification, including data discovery, scoping, and feasibility analysis for ETL/ELT activitiesConduct technical framing: perform feasibility studies, define needs, estimate effort, and plan project timelinesDevelop reusable, scalable ETL/ELT pipelines using PySpark, SQL, and Python on Google Cloud Platform (GCP)Experience in developing Talend Jobs, is considered a strong asset.Define Technical Prerequisites and template Developing reusable ETL Jobs use casesDesign and implement ingestion and transformation workflows using Cloud Composer (Airflow) and DataprocWrite clear and detailed technical specifications for data solutions and system componentsEnsure proper documentation of all work for operational maintainability and knowledge sharingHighly skilled in Microsoft SQL Server Stack (Data Base Engine)Advanced SQL, Query performance tuning SkillsContribute to and enrich a catalogue of reusable data solution components and templatesIdentify, design, and implement internal process improvements including infrastructure re-architecture, automation, and performance tuningBuild and operate infrastructure for efficient extraction, transformation, and loading of data using BigQuery, GCS, and DataprocBuild analytical data pipelines that provide actionable insights into business KPIs such as operational performance and customer behaviorProvide end-user support, assist stakeholders with data-related issues, and ensure customer satisfactionCustomer-oriented mindset with a strong focus on solution quality and reliabilityProactive, quality-driven approach to development, with a focus on best practices and continuous improvementCollaborate with business and technical teams (product, design, data, and executive) to support their data infrastructure needs.Build Data Warehouses, Data Marts Demonstrated experience working in cloud data engineering environments, especially with GCP data services: BigQuery, Google Cloud Storage, Dataproc, Pub/Sub, Looker, etc.Strong experience with distributed data processing using Apache Spark / PySparkAdvanced SQL skills with a focus on performance and optimization in cloud-native warehousesFamiliar with CI/CD pipelines and infrastructure as code tools (Cloud Build, Git, Terraform) for automated deployment and testingProficient in row-level security, access control management, and secure data deliveryExperienced in managing ETL feeding mechanisms: delta loads, full loads, and historical data backfillsSkilled in building data lakes, data marts, and OLAP models using GCP-native toolsKnowledge of Agile methodology and active participation in cross-functional teams and ceremoniesAble to collaborate across multiple interfaces in an international context, handling complexity and scale

QUALIFICATION, CERTIFICATION & EDUCATIONAL REQUIREMENTS

Tools :

Google Cloud Platform (GCP): BigQuery, Cloud Storage, Cloud Composer (Airflow), Dataproc, Pub/Sub, LookerPython, PySpark, SQL, Jupyter NotebooksGitLab, Cloud Build, Terraform, dbt (data build tool)Visual Studio Code, BigQuery UI, Looker Studio

Expertises :

Strong understanding of CI/CD pipelines and DevOps for data workflows using Cloud Build, Git, and TerraformProficient in data modeling for analytical solutions in BigQuery and LookerExpertise in data pipeline development using Apache Airflow, PySpark, and SQLHands-on experience with data lake and lakehouse architectures on GCPFamiliarity with Agile methodologies (Scrum/Kanban) for iterative deliveryUnderstanding of data governance, security (IAM, RLS), and monitoring on cloud platforms

Educational : In a master’s degree in computer science/statistics/mathematics fields

PREFERRED SKILLS

Fluency in English, both written and verbal.Excellent communication and presentation skills.Team work and willing to perform trough cooperation.Strong attention to details.Willingness for change and solving problem.Be passionate about digital transformation.Strong analytical mindset.Data oriented.

#LI-AC2

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