• Lead the design, development, and implementation of D365 CRM solutions, ensuring alignment with business requirements and best practices. • Collaborate with clients to gather and analyze requirements, translating them into technical specifications and solutions. • Develop and customize D365 CRM applications, including workflows, plugins, and integrations with other systems. • Provide technical guidance and support to junior consultants and team members, fostering a collaborative and knowledge-sharing environment. • Conduct system testing, troubleshooting, and performance tuning to ensure optimal system functionality and user experience.

Role Description The Gen AI Engineer within the Transformation Office serves as the hands-on architect of the enterprise data supply chain for the organization’s most advanced analytics, data science, and AI initiatives. This role performs the critical engineering work required to deliver high-fidelity, production-grade data that powers machine learning models, feature stores, and generative AI applications. - Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on-premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for consumption in Snowflake and downstream AI services. - Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams. - Engineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments. - Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads. - Executes non-invasive data extraction strategies to unlock mission-critical data from decades-old legacy systems while preserving system stability and avoiding disruption to core business operations. - Designs and manages complex, cross-platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory to ensure reliable, synchronized data movement across the organization’s multi-cloud architecture. - Partners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges—including PrivateLink, IAM, network segmentation, and firewall controls—while securing production approval for new data integrations. - Implements automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues that could negatively impact production analytics, machine learning, or AI systems. - Drives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance in Snowflake, AWS, and Azure, ensuring responsible cost management and measurable ROI for Transformation Office initiatives. - Operates as a dedicated engineering partner to MLOps, Data Science, and AI teams, rapidly iterating on evolving data requirements and translating experimental use cases into scalable, production-ready data solutions. Qualifications - Master’s degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred. - Six (6) years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments or equivalent combination of education and experience required. - Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services. - Advanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services. - Mastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization (Docker). - Proven ability to interface with "old world" tech (on-premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption. - A strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines). - A "get-it-done" attitude, capable of navigating enterprise bureaucracy and technical debt to ship code at the speed required by a Transformation Office. - Ability to work in a team environment. - Ability to meet or exceed Performance Competencies. Requirements - Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem solving, analysis, and discretion. - Ability to handle work-related stress; ability to handle multiple priorities simultaneously; and ability to meet deadlines. - Computer keyboarding, travel as required. - Hearing, vision, and talking. Benefits - Work-life balance. - Opportunities for career growth. - Caring culture.

Role Description L'ingénieur de données contribue à la mise en place des actifs de données et des solutions corporatives de gestion des données, notamment les entrepôts de données, les comptoirs de données, les solutions de gestion des données de référence ainsi que les solutions d'intégration de données. - Participer à la réalisation des solutions de gestion de données du secteur en collaboration avec les différentes parties prenantes. - Coordonner la collecte, la formalisation et la documentation des besoins de l'organisation en matière de gestion des données. - Comprendre et valider les besoins exprimés par l'équipe en étroite collaboration avec le Product Owner (PO) et les utilisateurs. - Participer à la conception, à la programmation, à la configuration et à l'intégration des solutions en appliquant l'approche "juste assez, juste à temps". - Utiliser les meilleures pratiques de développement recommandées par les Centres d'excellence (COE). - Appliquer les bons patrons de conception, de découpage et de modélisation pour les traitements, les données et l'infrastructure d'hébergement. - Assurer la sécurité et la confidentialité des informations numériques. - Développer des essais automatisés et en favoriser l'adoption. - Réaliser les essais requis tout au long du cycle de développement jusqu'à la mise en production. - Mesurer la qualité des solutions développées (performance, robustesse, couverture de tests, etc.). - Mettre en place et utiliser des pipelines d'intégration et de déploiement continus (CI/CD). - Communiquer et discuter des choix techniques avec les membres de l'équipe. - Participer à la révision et à la validation du code produit par les pairs. - Assurer le soutien technique des solutions en production (anomalies, entretien, surveillance, etc.). - Contribuer à la création et au maintien de la documentation nécessaire à la pérennité des solutions. Qualifications - Minimum de 5 années d'expérience pertinente en tant que développeur ETL et analyste de données. - Minimum de 3 années d'expérience avec Azure Data Factory (ADF). - Expérience avec des technologies de gestion de données telles que Snowflake, Power BI et autres outils comparables. - Bonne connaissance des principes Agile et DevOps (Scrum, Lean) et capacité de les appliquer dans un contexte de travail collaboratif. - Connaissance des technologies infonuagiques, de la virtualisation, des conteneurs et d'Azure DevOps. - Connaissance des patrons de conception, d'architecture et de découpage. - Bonne maîtrise des concepts de gestion et d'intégration de données ainsi que des techniques de développement de solutions de données. - Expérience comme analyste de données dans des projets décisionnels : entrepôts de données, comptoirs de données, lacs de données, etc. - Excellente capacité d'analyse et de résolution de problèmes. - Bon communicateur et esprit d'équipe. - Flexibilité et autonomie dans la gestion des priorités et des relations professionnelles. - Connaissance de l'industrie de l'assurance ou des services financiers (atout).

Role Description The Enterprise Data Architect is responsible for advancing the role of data architecture at OIT and across state agencies. - Advise, develop and facilitate data architecture strategy including cloud, advanced technologies & platforms for OIT and partner agencies. - Establish, schedule, and guide roadmaps through discovery, definition, design, and transition to development. - Create and document conceptual and logical models, logical solutions, and physical data models. - Lead efforts as an expert and leverage technology, innovation, and thought leadership to provide scalable solutions to complex and diverse business problems. - Approve all relevant data architecture decisions and align with agency needs. Qualifications - 10+ years subject matter experience. Company Description