Job Description As a Scala Data Engineering Architect at Publicis Sapient, you will lead the design and implementation of modern, cloud-native data platforms that power large-scale digital transformation. This role combines hands-on architecture and team leadership, enabling organizations to unlock the full potential of their data using AWS and Scala-based technologies. Your Impact Architecture & Strategy Define end-to-end data architecture strategies leveraging AWS and Scala , ensuring scalability, reliability, and alignment with business objectives. Lead the selection and application of data technologies, frameworks, and patterns tailored to business needs. Develop and maintain architectural roadmaps for data platform modernization and cloud-native initiatives. Solution Design & Delivery Translate business requirements into robust, scalable data solutions using AWS-native services and Scala-based frameworks. Design and implement data ingestion, processing, storage, and analytics pipelines with high availability and performance. Build reusable components and frameworks to streamline development and accelerate delivery. Technical Leadership Provide architectural guidance and mentorship to data engineering teams. Review solution designs to ensure adherence to engineering best practices and standards. Support project estimation and contribute to delivery plans and technical roadmaps. Client Engagement & Collaboration Collaborate with business and technical stakeholders to align data strategies with organizational goals. Facilitate architecture reviews, technical deep dives, and collaborative design sessions. Operational Excellence Oversee the performance, observability, and automation of data platforms in production environments. Drive continuous improvements in platform health, data quality, and operational efficiency. Qualifications Your Skills & Experience Proven experience leading data engineering teams and delivering cloud-native data platforms on AWS . Strong programming expertise in Scala , particularly for distributed data processing and ETL workflows. Hands-on experience with AWS services including S3, Glue, EMR, Lambda, Redshift, Athena , and DynamoDB . Deep understanding of data modeling, data warehousing, and stream/batch data processing frameworks (e.g., Apache Spark ). Familiarity with infrastructure-as-code and CI/CD for data pipelines (e.g., Terraform, Git, Jenkins ). Strong communication skills and stakeholder engagement experience in client-facing environments. Set Yourself Apart With Experience implementing DataOps and DevOps practices in cloud data environments. Exposure to multi-cloud or hybrid cloud architectures (AWS, GCP, Azure). Knowledge of observability , logging, and performance optimization strategies for data platforms. #J-18808-Ljbffr