Keyrus

Data Engineer

• Design, develop, and implement scalable, efficient data pipelines using Azure Databricks, Apache Airflow, and PySpark
• Ensure data quality, performance, and reliability
• Perform data analysis and troubleshooting
• Create interactive reports and dashboards in Power BI
• Actively collaborate on the design and implementation of new features and capabilities

Data Engineer, Azure

Data Scientist

• Identifier, cadrer et formaliser des cas d’usage Data Science
• Proposer et développer des POCs innovants
• Concevoir et entraîner des modèles de Machine Learning / Deep Learning
• Traiter et structurer des données complexes, parfois textuelles ou non structurées
• Collaborer avec les Data Engineers pour l’industrialisation des pipelines
• Participer aux activités R&D IA de Keyrus

Full-stack Engineer

• Define and evolve AI architecture choices: ML, deep learning, generative AI, LLMs
• Design robust, scalable AI solutions aligned with business objectives
• Ensure the technical quality of deliverables and oversee code reviews
• Drive technology choices and maintain active monitoring of AI innovations
• Establish engineering best practices (testing, code review, standards)
• Lead and develop a team of AI Engineers (junior to senior profiles)
• Structure AI engineering practices: code standards, MLOps, documentation
• Contribute to the recruitment and career development of AI profiles within Keyrus
• Run technical workshops and share knowledge within the practice
• Manage end-to-end production deployment of AI use cases (from POC to production)
• Design and implement advanced RAG architectures (ingestion, indexing, generation)
• Optimize GenAI systems (prompt engineering, fine-tuning, evaluation)
• Implement data pipelines and model monitoring systems
• Interact directly with clients: scoping, reporting, strategic advisory
• Ensure performance, security, and cost optimization of deployed AI solutions

Tech Lead AI

Product Manager

• Gérer et optimiser une plateforme data en collaboration avec des équipes pluridisciplinaires et multiculturelles
• Décliner la stratégie produit et assurer son amélioration continue
• Gérer la priorisation, le backlog et le suivi budgétaire
• Concevoir les fonctionnalités, les processus associés et rédiger les user stories
• Assurer la coordination et la communication avec les parties prenantes
• Promouvoir l’usage et la connaissance de la plateforme auprès des équipes projet et des stakeholders
• Recueillir et formaliser les besoins métier pour les intégrer à la stratégie produit
• Garantir la conformité des livrables avec les besoins initiaux (tests, qualité)
• Définir et suivre les KPI de la plateforme

Product Owner, Data

• Delivery / Conseil & Expertise: Conception et mise en œuvre d'architectures Data modernes (Cloud & On-premise)
• Réalisation d’audits, définition de roadmaps, accompagnement sur les choix technologiques
• Réalisation de PoC/MVP, coaching technique, conduite du changement
• Participation à l’écosystème tech (Meetups, afterworks, partenaires, éditeurs…)
• Contribution active à notre communauté d’experts Data (Snowflake, AWS, GCP, Azure, Databricks, DBT, Looker, Rivery, Airbyte, etc.)
• Accompagnement des équipes commerciales sur les réponses techniques
• Participation à la formalisation des offres et des convictions Data de l’agence
• Accompagnement de consultants en mission, animation de la communauté, recrutement, entretiens techniques

Data Architect

• Guidage des clients vers les solutions Data les plus pertinentes.
• Conception des écosystèmes robustes (Cloud, stockage, intégration).
• Transformation des données en insights stratégiques.
• Garantie de la qualité technique et fonctionnelle des solutions.
• Participation à la formation des équipes.

Tech Lead, Data

• Conceive, develop and industrialize reliable and high-performance data pipelines
• Integrate structured and unstructured data, both in batch and real-time
• Deploy and optimize data environments on the Cloud (AWS, Azure, GCP)
• Implement and orchestrate ETL/ELT processes (Talend, Airflow, dbt, Fivetran, etc.)
• Work on modern environments such as Databricks, Snowflake, BigQuery, Redshift
• Ensure data quality, security and governance
• Contribute to technology watch and recommend the best solutions for each project

Data Analyst

• Développement des flux ETL : Création et optimisation de pipelines de données robustes.
• Création de Dashboards interactifs et mise en place de KPIs : Rendre les données compréhensibles et exploitables.
• Support & Maintenance des reportings : Assurer une gestion efficace des flux.
• Gestion de Projet : Participer activement à la gestion de projet en toute autonomie.
• Méthode Agile : Travailler avec les équipes dans un environnement agile.
• Veille technologique & Certification : Rester à la pointe de la technologie et enrichir tes compétences.
• Accompagnement des juniors : Partage de ton expertise et développement des talents de demain.

• Design, implement, and maintain enterprise data warehouse solutions following Data Vault 2.0 methodology
• Develop and optimise Hubs, Links, Satellites, PIT, and Bridge tables
• Lead conceptual, logical, and physical data modelling activities across multiple initiatives
• Define and promote data architecture standards, modelling guidelines, naming conventions, and documentation practices
• Ensure scalability, performance, reliability, and maintainability of data warehouse architectures
• Provide technical leadership and architectural guidance to Data Engineers and development teams
• Drive the standardisation and automation of ELT processes, metadata management, and deployment practices
• Collaborate closely with Business Analysts, Product Owners, Data Governance teams, and other stakeholders to translate business requirements into robust data solutions
• Support reporting and analytics teams by delivering optimised and business-ready data structures

Senior Data Warehouse Architect, Data Vault 2.0

• Collaborate with clients to understand business objectives and technical requirements, translating them into effective data solutions that drive value.
• Support end-to-end delivery of data initiatives: design, build, and optimize scalable data pipelines, data lakes and architectures using Fabric, and other modern tools (Databricks, Snowflake, Azure Data Services, etc.).
• Design and implement lakehouse architectures, following medallion architecture principles to ensure scalability, data quality, governance, and performance.
• Build and maintain parameterized ingestion frameworks for batch and near real-time pipelines.
• Design, implement, and operate CI/CD pipelines to enable reliable, automated deployments across environments.
• Define, promote, and enforce Fabric best practices.
• Work closely with cross-functional teams —data engineers, analysts, and business stakeholders—to deliver cohesive and impactful solutions.
• Stay up to date with offerings and emerging technologies, recommending improvements and innovative solutions to meet business needs.

Data Engineer Consultant – Microsoft Fabric

SDET

• Design, develop, and execute data validation and automation test cases
• Build and maintain SQL queries and scripts to validate large datasets
• Perform API testing and backend validation across data pipelines
• Develop automation frameworks or reusable components (Python or Selenium-based)
• Collaborate with BAs, data engineers, and SMEs to ensure data accuracy and consistency
• Identify data quality issues and perform root cause analysis
• Contribute to CI/CD testing pipelines (e.g., Jenkins)
• Ensure adherence to testing standards, data quality, and SDLC best practices

ETL & SQL Test Automation Engineer, Python, Selenium

Support Engineer

• Support the administration and operation of middleware environments, such as Application Servers, Integration Servers and Web Servers;
• Perform application deployments following approved procedures and change windows;
• Monitor environments, analyze alerts and assist in identifying and resolving incidents;
• Execute operational routines, including starting/stopping services, post-deployment validations and log analysis;
• Support initial problem analysis and escalate when necessary;
• Participate in change requests (GMUDs) following change processes and internal controls;
• Keep technical documentation up to date, including procedures and playbooks;
• Support internal development and infrastructure teams and vendors on middleware-related activities;
• Follow best practices for security, stability and availability of environments.

Middleware Support Analyst – Junior

• Understand client needs and lead delivery end‑to‑end — proposing solutions that solve real business problems across ingestion, transformation, governance, and activation
• Build and manage scalable pipelines using Snowflake, Fivetran, Gong REST APIs, and Snowpipe
• Configure AWS S3 raw landing zones and Snowflake External Stages to ensure a durable, immutable raw data layer
• Deal with semi-structured data and expertly manage JSON/VARIANT payloads in Snowflake, implementing "schema-on-read" patterns to protect against upstream API changes
• Architect complex dbt models to parse, flatten, and normalize VARIANT data into structured tables
• Implement logic to merge issuer entities, contact records, and learner profiles across disparate systems to create a "Golden Record"
• Build semantic layers for predictive metrics, including Engagement Scores, Sentiment Trends, and Churn Risk
• Leverage Snowflake Cortex AI for predictive scoring and surfacing insights
• Maintain Governance and Security by implementing Role-Based Access Control (RBAC), PII masking (Dynamic Data Masking), and Row-Level Security (RLS) across the Snowflake environment
• Support clients in their data and digital transformation journeys

Senior Data Engineer – Snowflake

Consultant

• Design, build, and maintain enterprise-grade Power BI dashboards and reports
• Work closely with business stakeholders to translate requirements into scalable data models
• Optimize report performance (DAX, model design, query folding, etc.)
• Support and enhance reporting pipelines connected to Databricks and other sources
• Develop and maintain data pipelines and transformations in Databricks
• Work with structured and semi-structured data sources
• Support medallion-style architectures (Bronze/Silver/Gold)
• Troubleshoot data quality, performance, and pipeline issues
• Perform QA validation across reports and data pipelines
• Identify gaps between business expectations and delivered outputs

Senior Data Consultant – Power BI, Databricks

Architect

• Escolher padrões arquiteturais adequados (microsserviços, filas de mensagens, etc.).
• Justificar a escolha dos padrões em relação aos requisitos.
• Considerar os trade-offs entre simplicidade e flexibilidade.
• Definir estratégias claras para lidar com o aumento da carga (escalabilidade).
• Implementar mecanismos de recuperação de falhas (resiliência).
• Atender ao requisito de não indisponibilidade do serviço de controle de lançamentos em caso de falha do consolidado diário.
• Definir como os componentes se comunicam (protocolos, formatos de mensagem, etc).
• Escolher ferramentas de integração adequadas e suas responsabilidades.
• Implementar autenticação, autorização e criptografia (segurança).
• Implementar mecanismos de proteção contra ataques (WAF, etc).
• Analisar a qualidade da divisão do sistema em partes menores e gerenciáveis.
• Abstrair e segregar responsabilidades entre as camadas.
• Garantir a aderência às boas práticas de desenvolvimento (Solid, Clean Code, etc).
• Escrever código legível, organizado e de fácil manutenção.
• Utilizar a linguagem C# de forma eficiente.
• Implementar testes com boa cobertura, qualidade e relevância.
• Cumprir os requisitos técnicos obrigatórios.
• Atender aos requisitos de negócio.