Role Description We are looking for a Tech Lead DataOps to join a large-scale, international program focused on building and scaling the Group’s Enterprise Cloud Data Warehouse. The platform centralizes global data across major business domains (Finance, Customers, Operations, etc.) using modern cloud data architectures and Agile/Scrum methodologies. In this role, you will lead the transversal platform and reliability efforts, driving industrialization, automation, CI/CD pipelines, and overall platform stability in a fully remote, multicultural environment. Main Responsibilities - Technical Leadership & DataOps Governance - Define and evolve technical standards, ensuring the reliability, scalability, and performance of data workflows. - Lead industrialization and automation initiatives across development and deployment processes, supporting multiple squads on DataOps best practices. - Contribute to technical roadmap definitions, architecture decisions, and continuous ecosystem improvements. - Automation & Platform Engineering - Design, maintain, and evolve internal development and deployment tooling around dbt , Airflow , and Snowflake . - Develop and optimize internal CLI tools for automated dbt model generation, YAML testing, DAG creation, and deployment automation. - Contribute to the integration of AI/LLM capabilities into development and DataOps workflows to reduce manual operations. - CI/CD & Deployment Engineering - Design, implement, and maintain secure and automated multi-environment Data CI/CD pipelines. - Ensure deployment quality during release cycles, collaborating with project squads and supporting release governance. - Orchestration, Reliability & Operations - Supervise, optimize, and design Apache Airflow orchestration workflows and execution DAGs. - Implement monitoring, alerting, and observability capabilities to maximize platform stability and operational efficiency. - Contribute to incident resolution and root cause analysis. Qualifications - Methodology: Strong knowledge and hands-on experience with Data Vault 2.0 methodology applied to enterprise data platforms. - Core Tech Stack: Advanced proficiency in Python (focused on data platforms/automation) and SQL query optimization. - Data Ecosystem & Orchestration: Solid hands-on experience with dbt and Apache Airflow (including DAG design and workflow optimization). - CI/CD & Cloud Infrastructure: Good knowledge of DevOps practices using Git/GitLab , Jenkins , Docker , Kubernetes , and Snowflake (or equivalent MPP platforms). - Leadership & Soft Skills: Strong transversal technical leadership, an autonomous/proactive mindset, and excellent communication skills to collaborate with multicultural, distributed teams. - Languages: Professional proficiency in both English and French is required. Nice to have skills - Experience with specific data modeling and automation tools like AutomateDV and DBSchema . - Practical exposure to modern observability and data platform reliability practices. - Experience or strong interest in integrating AI/LLM tooling (such as GitHub Copilot) into DataOps development and deployment workflows. Contract Type Long term contract

• Lead end-to-end delivery of large-scale, scope-based data platforms and cloud migration projects, from initiation through closure. • Provide technical oversight and act as a bridge between engineering teams and client stakeholders, ensuring alignment on scope, timelines, and quality. • Oversee technical prerequisites and delivery readiness, including environment access, data ingestion setup, CDC analysis, SLAs, and validation processes. • Drive stakeholder communication, risk management, issue resolution, and milestone-based sign-offs. • Lead and support cross-functional engineering teams through clear prioritization, performance management, and delivery governance. • Own project financials, including forecasting, cost control, invoicing approvals, and scope change management. • Prepare and present regular project status reports, highlighting progress, risks, dependencies, and key metrics. • Support pre-sales and growth initiatives by contributing to estimations, delivery strategies, and follow-on phase planning. • Partner with recruiting teams to support hiring and onboarding for specialized technical roles.

• Research, recommend, and implement the optimal combination of technologies for the infrastructure dashboard data platform (e.g., databases, processing frameworks, orchestration tools) • Set up necessary data storage solutions with a focus on long-term scalability • Connect live data via API or other appropriate mechanisms from multi-domain sources • Collaborate with domain experts to understand Research, Development, Test & Evaluation (RDT&E) data being ingested • Ensure modeling controls for data abnormalities and structural differences • Build a robust data pipeline for loading and transforming data into a model appropriate for STF use, considering both short- and long-term data goals • Create and enforce data governance policies, including data quality standards, data lineage tracking, and metadata management • Communicate these policies to data analysts and stakeholders • Monitor and optimize database and/or data model performance and security • Conduct periodic testing of pipeline health and data validity • Work with data stakeholders and the data analyst team to ensure the background data and architecture meet the needs of requested visualizations and analyses • Create and maintain detailed documentation for data architecture, data models, pipeline jobs, and all related processes

• Lead the technical execution of the migration from Oracle and Boomi to Azure SQL and Azure Data Factory. • Guide architecture and design decisions across database migration, ETL migration, integration, validation, and deployment activities. • Serve as the main technical point of contact with the client. • Translate client requirements, priorities, and constraints into actionable technical direction for the team. • Coordinate the work of the database migration and ETL / ADF engineers. • Review and validate technical designs, source-to-target mappings, migration approaches, and implementation plans. • Provide hands-on technical guidance and troubleshooting support when needed. • Help resolve complex issues related to data conversion, pipeline design, performance, compatibility, validation, and reconciliation. • Track project timeline, deliverables, dependencies, risks, and open decisions. • Ensure the team is aligned on priorities, scope, quality expectations, and delivery commitments. • Communicate project status, risks, blockers, and key decisions to client stakeholders and internal leadership. • Support planning for testing, production cutover, rollback considerations, and post-migration stabilization. • Ensure technical documentation is created and maintained throughout the engagement. • Promote best practices for data migration, ETL design, security, performance, monitoring, and maintainability.