Technical Lead Data (TGQF)

Job Description

What you'll do

• 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

What you'll bring to the team

Education :

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

Relevant Experience :

• 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.

Skills and Knowledge :

Technical Expertise

• 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.

Technical Assets

• 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.

Professional Competencies

• 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.