Data Engineering Manager

Plus de 8 ans d'expérience pratique en gestion des données, ingénierie des données, architecture des données Plus de 5 ans d'expérience en SQL, Python et Excel Plus de 2 ans d'expérience dans les services financiers (conseil en gestion sectorielle ou financière) Plus de 2 ans d'expérience en visualisation de données (Power BI et/ou Tableau) Plus de 2 ans d'expérience dans la gestion d'une équipe à l'étranger Plus de 2 ans d'expérience avec les systèmes basés sur le nuage (Azure, AWS ou GCP) Plus de 2 ans d'expérience en architecture de données d'entreprise (comme l'approvisionnement de données de bout en bout, le processus ETL/ELT de pipeline, la conservation, la qualité des données) Plus de 2 ans d'expérience dans la conduite de solutions technologiques de bout en bout. Capacité de voyager au moins 25 %. Compétences privilégiées : Posséder des certifications AWS, Azure et GCP pour compléter une vaste expérience pratique. Expérience pratique de la science des données (IA/ML/Gen AI) Expérience pratique des outils MDM, DQ, DG (par exemple, Collibra, Informatica, Alteryx, Microsoft Purview, etc.) Expérience de l'utilisation de R (ou d'outils similaires) Expérience précieuse du conseil en gestion « Big 4 » ou exposition à plusieurs secteurs d'activité. Diplôme de premier ou de deuxième cycle préféré. Data Engineering Manager As a Data Engineering Manager for this onsite project, you will manage an offshore team (of data engineers, data architects and data scientists), as well as ensure successful project execution in this client-facing role. 8+ years of hands-on experience across Data Management, Data Engineering, Data Architecture 5+ years of SQL, Python, and Excel experience 2+ years of Financial Services experience (industry or FS management consulting) 2+ years of Data Visualization experience (Power BI and/or Tableau) 2+ years of experience managing an offshore team 2+ years of experience with Cloud-based systems (Azure, AWS, or GCP) 2+ years of Enterprise Data Architecture experience (such as end to data sourcing, pipeline ETL/ELT process, curation, data quality) 2+ years of experience driving end-to-end technology solutions. Ability to travel at least 25%. Possess certifications in AWS, Azure, and GCP to complement extensive hands-on experience. Demonstrated expertise with certifications in Snowflake. Hands-on experience with data science (AI/ML/Gen AI) Hands-on experience with MDM, DQ, DG tools (e.g., Collibra, Informatica, Alteryx, Microsoft Purview, etc.) Experience using R (or similar tools) Valuable "Big 4" Management Consulting experience or exposure to multiple industries. Undergraduate or graduate degree preferred. Will help lead the development and articulate long-term strategic goals for data architecture vision and establish data standards for Non-Financial Risk systems and processes Will coordinate between Non-Financial Risk business stakeholders and technical teams. Develop and articulate long-term strategic goals for data architecture vision and establish data standards for enterprise systems. Utilize various cloud technologies, including Azure, AWS, GCP, and data platforms like Databricks and Snowflake. Conceptualize and create an end-to-end vision outlining the seamless flow of data through successive stages. Institute processes for governing the identification, collection, and utilization of corporate metadata, ensuring accuracy and validity. Implement methods and procedures for tracking data quality, completeness, redundancy, compliance, and continuous improvement. Evaluate and determine governance, stewardship, and frameworks for effective data management across the enterprise. Develop comprehensive strategies and plans for data capacity planning, data security, life cycle data management, scalability, backup, disaster recovery, business continuity, and archiving. Identify potential areas for policy and procedure enhancements, initiating changes where required for optimal data management. Formulate and maintain data models and establish policies and procedures for functional design. Offer technical recommendations to senior managers and technical staff in the development and implementation of databases and documentation. Stay informed about upgrades and emerging database technologies through continuous research. Collaborate with project managers and business leaders on all projects involving enterprise data. Document the data architecture and environment to ensure a current and accurate understanding of the overall data landscape. Design and implement data solutions tailored to meet customer needs and specific use cases. Provide thought leadership by recommending the most suitable technologies and solutions for various use cases, spanning from the application layer to infrastructure.