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

Role Description The Data Product Analyst role is critical to ensuring that modernization delivers tangible business value. This role will design, build, and operate reliable data pipelines that ingest and integrate data into the DMP, apply standardized transformations, enforce data quality and governance controls, and produce trusted, analytics-ready datasets that support regulatory oversight, compliance monitoring, and evidence-based decision-making aligned with DRAS objectives. This position will primarily support the Digital Regulatory Assurance System (DRAS) program, where high quality, timely analytics are essential to regulatory and compliance functions. As data and analytics maturity increases, the role may be expanded to support additional enterprise data initiatives. Duties - Design and implement scalable, secure, and high-performance data architecture on Microsoft Azure, supporting both cloud-native and hybrid environments. - Lead the development of data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. - Work with the Data Architect and manage data lakes and structured storage solutions using Azure Data Lake Storage Gen2, ensuring efficient access and governance. - Integrate data from diverse source systems including ServiceNow and geospatial systems, using APIs, connectors, and custom scripts. - Develop and maintain robust data models and semantic layers to support operational reporting, analytics, and machine learning use cases. - Build and optimize data workflows using Python and SQL for data cleansing, enrichment, and advanced analytics within Azure Databricks. - Design and expose secure data services and APIs using Azure API Management for downstream systems. - Implement data governance practices, including metadata management, data classification, and lineage tracking. - Ensure compliance with privacy and regulatory standards (e.g., FOIP, GDPR) through role-based access controls, encryption, and data masking. - Monitor and troubleshoot data pipelines and integrations, ensuring reliability, scalability, and performance across the platform. - Utilize AI and automation tools to streamline data engineering workflows, including pipeline development, testing, monitoring, and documentation. - Leverage AI-assisted tools for code generation, optimization, and review to improve development efficiency and code quality. - Design and curate standardized, high-quality datasets that are suitable for advanced analytics and future AI use cases. - Other duties as needed. Company Description

Role Description We are looking for an experienced Product Owner with strong expertise in Snowflake or Databricks to lead the delivery of an enterprise data lake initiative. This role requires a mix of product ownership, technical understanding, and data platform experience. The ideal candidate will work closely with engineering, architecture, and business teams to translate requirements into actionable deliverables and ensure successful rollout of a scalable data platform. - Lead the delivery of an enterprise data lake built on Snowflake or Databricks from initial setup to production deployment. - Translate business and data requirements into clear user stories, acceptance criteria, and sprint plans. - Drive the creation of data ingestion and integration pipelines from SaaS applications and other enterprise systems. - Manage the full development lifecycle, ensuring milestones and deliverables are completed on time. - Prioritize the product backlog based on business value, technical feasibility, and operational needs. - Build and maintain a detailed delivery roadmap, including MVP and phased releases. - Collaborate with cloud infrastructure and DevOps teams to ensure scalable, secure, and cost-effective environments. - Track and report key performance indicators such as pipeline success rates, data processing speed, and platform reliability. - Ensure smooth handover to analytics and data science teams with proper documentation and operational readiness. Qualifications - Proven experience as a Product Owner or Product Manager in data platforms, analytics, or cloud-based systems. - Strong understanding of Snowflake or Databricks including data modeling, pipelines, and cloud architecture. - Experience working with engineering, data, and DevOps teams in Agile environments. - Ability to translate complex business needs into technical execution plans. - Familiarity with data ingestion, ETL or ELT workflows, and API based integrations. - Strong communication skills and the ability to collaborate with cross-functional stakeholders. - Previous experience in HRMS or large-scale enterprise environments is an advantage.