Company description Publicis Sapient is a digital transformation partner supporting established organizations in achieving a future, digitally enabled state, both in how they operate and how they deliver value to their customers. We help unlock value through a start-up mindset combined with modern, proven methods. Our approach integrates strategy, consulting, and customer experience with agile engineering and creative problem-solving to deliver measurable business outcomes. Guided by our core values and our purpose, helping people thrive in the brave pursuit of what’s next—our global team of more than 20,000 professionals across 53 offices brings together expertise in technology, data science, consulting, and customer-centricity. By combining these capabilities, we enable our clients to accelerate their businesses through the design and delivery of products and services that their customers truly value. Overview We’re looking for a Data Engineer Specialist who combines deep technical expertise with a genuine passion for people, leadership, and building exceptional products. In this hands-on leadership role, you’ll have the opportunity to lead and grow a team of data engineers while staying close to the technology. You’ll influence architecture, engineering standards, and delivery practices, and help build reliable, scalable data products that solve meaningful challenges. You’ll thrive in this role if you enjoy turning complex data problems into elegant solutions, taking ownership from idea to production, and helping other engineers do their best work. You’ll work with a modern cloud data stack—including GCP, Python, advanced SQL, Kafka, Cloud Composer, Terraform, and CI/CD—with the space to make technical decisions and continuously improve how the team builds and delivers. Responsibilities Lead, mentor, and empower a team of data engineers, providing technical direction, guiding architecture decisions, and creating an environment where people can grow and do their best work. Take ownership of scalable data solutions from design and implementation through deployment, monitoring, optimization, and production support. Design, build, and optimize robust ETL/ELT pipelines across staging, warehouse, and data mart layers. Use advanced SQL to create high-performing transformations and define effective partitioning and clustering strategies. Shape warehouse architecture and schema design, including dimensional modelling and slowly changing dimension (SCD) patterns. Build Python connectors, orchestration scripts, reusable services, and data contract validation logic. Develop reliable event-driven and streaming solutions with Apache Kafka, with a strong focus on version-controlled, testable transformations and built-in assertions. Embed automated data quality and contract testing into the engineering lifecycle so issues are identified early and releases remain dependable. Build secure, scalable, and cost-efficient solutions on Google Cloud Platform (GCP), continuously balancing performance, reliability, and cloud spend. Use Cloud Composer / Apache Airflow to orchestrate workflows, schedules, and dependencies across the data ecosystem. Automate cloud infrastructure with Terraform or similar Infrastructure as Code (IaC) tooling, covering areas such as storage, datasets, IAM, and networking. Strengthen engineering practices through Git-based CI/CD, code reviews, automated testing, and controlled deployments using tools such as Cloud Build or GitHub Actions. Help shape standards for code quality, testing, documentation, observability, security, reliability, and data governance. Collaborate with architects, product teams, analysts, data scientists, and engineers to turn complex requirements into maintainable data products with tangible impact. Skills & Experience Significant hands-on experience in Data Engineering, designing and delivering production-grade data platforms and pipelines. Experience leading, mentoring, or technically guiding engineers while remaining actively involved in architecture and development. Strong SQL expertise, including complex transformations, query optimization, partitioning, and clustering across staging, warehouse, and data mart layers. Solid knowledge of data warehousing, dimensional modelling, schema design, and slowly changing dimensions (SCD). Strong hands-on Python skills for connector development, data processing, automation, orchestration, and validation frameworks. Practical experience building cloud-native data solutions on GCP, with an understanding of performance and cost optimization. Hands-on experience with Apache Kafka and event-driven or streaming architectures. Experience building automated data quality, contract-validation, and transformation tests as part of version-controlled engineering workflows. Additional information Besides an exciting job, here's what you can expect: A fast-paced tech environment Continuous growth & learning Open feedback culture Room for own initiative & ideas Transparency about results & strategy Recognition & reward for hard work Working with a flexible schedule Medical subscription Meal tickets Extra vacation days - starting with 25 vacation days Many others perks