Data Engineer

h3About the Role /h3 pWe are looking for a Data Scientist Data Engineer who can transform complex mobility, vehicle, and sensor data into reliable, scalable, and business-relevant analytical products. This position combines data science, data engineering, geospatial analytics, machine learning, and stakeholder collaboration to support the development of innovative mobility solutions. /p pYou will work with large-scale datasets originating from connected vehicles, sensors, and external data sources, helping to design analytical solutions that improve understanding of road conditions, vehicle behaviour, and mobility patterns. /p h3Key Responsibilities /h3 ul liExplore, clean, combine, and transform complex datasets from connected systems, sensors, and external sources. /li liDesign and maintain analytical workflows for mobility, road-condition, and vehicle-behaviour use cases. /li liPerform geospatial analysis, including map matching, spatial quality checks, coverage calculations, and trajectory processing. /li liDevelop, validate, and improve statistical and machine learning models using supervised and unsupervised techniques. /li liDefine data validation strategies and investigate anomalies, bias, inconsistencies, and coverage gaps. /li liConvert prototypes and proof-of-concepts into robust, maintainable, and production-ready data pipelines. /li liOperate and optimize cloud-based data workflows with a strong focus on automation, monitoring, and traceability. /li liDeliver analytical reports, dashboards, maps, and visualizations that support informed business decisions. /li liCollaborate with engineers, data providers, business stakeholders, and external partners to ensure data quality and alignment on requirements. /li liParticipate in Agile ceremonies, technical reviews, documentation, and knowledge-sharing initiatives. /li /ul h3Required Skills Experience /h3 h3Technical Skills /h3 ul liStrong experience with bPython /b and data analytics libraries such as bpandas /b, bNumPy /b, and bscikit-learn /b. /li liSolid understanding of bdata engineering concepts /b, including ETL/ELT processes, schema management, automated pipelines, and data quality controls. /li liExperience with bSQL /b, Git, and collaborative software development practices. /li liKnowledge of bgeospatial analytics /b using tools such as GeoPandas or similar technologies. /li liFamiliarity with cloud platforms, scalable compute environments, and container technologies. Experience with bAWS /b and bDocker /b is highly valued. /li liExperience building and validating machine learning models, including feature engineering, model evaluation, and uncertainty analysis. /li liStrong reporting and data visualization skills, with the ability to communicate findings effectively to non-technical audiences. /li /ul h3Nice-to-Have Experience /h3 pExperience in one or more of the following areas will be considered a strong advantage: /p ul liConnected vehicles, IoT, telemetry, transportation, infrastructure, or other sensor-driven environments. /li liTime-series analytics, computer vision, deep learning, or generative AI. /li liDigital twin technologies. /li liTaking proof-of-concepts through to production-grade data products. /li liScientific research, experimental design, or regulated environments. /li liValidation of algorithmic outputs using reference datasets or human-reviewed ground truth. /li /ul h3Languages /h3 ul liProfessional proficiency in bEnglish /b /li liGood command of bFrench /b /li /ul h3What We're Looking For /h3 pThe ideal candidate combines strong analytical skills with a pragmatic engineering mindset. You are able to work independently, navigate ambiguity, and translate complex technical findings into actionable insights. At the same time, you thrive in collaborative environments and enjoy working closely with experts from multiple disciplines. /p h3Key Competencies /h3 ul liAnalytical rigor /li liOwnership and accountability /li liPragmatism and solution orientation /li liClear communication /li liCollaboration and teamwork /li liAdaptability in evolving environments /li /ul