Technical Lead Data (TGQF)

Company Description Quality Foundations (QF) develops and operates cross-functional products, platforms, and services that enhance the quality, observability, performance, and operations of Ubisoft games through data, analytics, and artificial intelligence. In this context, the Lead Data Technical plays a key role in defining and evolving the data foundations that support QF’s various products. The incumbent serves as the primary technical authority for all matters related to data, data architectures, and analytics platforms within Quality Foundations. They act as the lead expert responsible for designing, evolving, and operating robust, scalable, and high-performance data solutions that meet the needs of development, production, analytics, and AI teams. Working closely with architects, development teams, and product leaders, this role contributes to defining, implementing, and evolving the organization’s target data architecture. The incumbent ensures the adoption of technical best practices, promotes consistency across the Quality Foundations product portfolio, and aligns data initiatives with the company’s strategic objectives.

Job Description

• Collaborate with architects and contribute to the design of new data products and services by proposing robust, scalable solutions aligned with organizational needs.
• Participate in the implementation of the target data architecture and drive its adoption across teams.
• Serve as the subject matter expert for all topics related to data, data architectures, and data processing pipelines.
• Provide technical leadership and expertise across all Quality Foundations products regarding data storage, modeling, governance, and processing.
• Define, maintain, and promote data standards and best practices within QF, ensuring their consistent application across teams.
• Propose, review, and validate technical and architectural decisions through Architecture Decision Records (ADRs), and ensure adoption of approved decisions.
• Actively contribute to the development and delivery of initiatives involving the highest levels of complexity or risk.
• Advise architects, project managers, and leaders on technology directions and opportunities to improve data platforms.
• Analyze and optimize the performance, cost efficiency, reliability, and scalability of data systems.
• Act as a subject matter expert in relational and non-relational database optimization.
• Collaborate with development, analytics, artificial intelligence, and operations teams to ensure seamless integration of data solutions.
• Ensure the technical quality of data pipelines and promote best practices in monitoring, alerting, and operations.
• Foster knowledge sharing, mentorship, and the development of technical autonomy within data development teams.
• Participate in the technical evaluation of new technologies, platforms, and data-related approaches.
• Perform any other related duties as required.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or an equivalent field of study.

• Minimum of 8 years of experience in software development or data engineering.
• Significant experience designing and implementing large-scale data platforms.
• Experience in technical leadership, team coaching, or mentorship.
• Experience with distributed architectures and large-scale data processing systems.

• Strong expertise in large-scale data solution development and SQL/NoSQL data modeling.
• Strong proficiency in one or more of the following languages: Python, PySpark, SQL, and Scala .
• Solid understanding of modern data architectures, data processing pipelines, and analytics platforms, including Medallion architectures (Bronze/Silver/Gold) and Lakehouse platforms (Databricks, Delta Lake) .
• Experience designing configurable and environment-agnostic solutions (development, staging, production), including pipeline-as-code and configuration management practices.
• Ability to design scalable, high-performance, and maintainable solutions.
• Experience documenting architectural and technical decisions.

• Knowledge of cloud services ( AWS, Azure, or equivalent ) as well as Docker and Kubernetes technologies.
• Knowledge of data orchestration frameworks such as Apache Airflow or Databricks Workflows .
• Knowledge of real-time data streaming technologies ( Spark Structured Streaming or equivalent).
• Knowledge of Databricks, Apache Spark (batch and streaming), Delta Lake, Elasticsearch/OpenSearch, SQL Server, and PostgreSQL .
• Knowledge of large-scale data platform administration and both relational and non-relational databases.
• Knowledge of monitoring, logging, and alerting systems for data pipelines.
• Understanding of machine learning and artificial intelligence concepts.
• Experience working in high-volume, real-time critical systems environments (telemetry, observability, monitoring) is considered a significant asset.

• Excellent analytical and problem-solving skills.
• Strong ability to solve complex technical challenges.
• Aptitude for mentoring and fostering technical autonomy within teams.
• Strong appreciation for configuration-driven approaches, including parameterized systems, declarative pipelines, and reproducible deployments (Infrastructure as Code).
• Excellent communication skills and ability to explain complex technical concepts to diverse audiences.
• Influential leadership and the ability to drive adoption of best practices across teams.
• Ability to work effectively in a multidisciplinary environment.
• Strong initiative and autonomy.
• Results-oriented mindset with a focus on continuous improvement.
• Ability to manage multiple priorities simultaneously and make sound prioritization decisions.