Join Deloitte's Technology & Transformation practice and leverage your expertise to drive data modernization and migration projects. This role focuses on unlocking the value within vast datasets, providing strategic guidance and implementation services to help clients manage data from disparate sources and transform it into actionable insights.

As a Senior GCP Data Engineer, you will be instrumental in managing client relationships and ensuring the successful delivery of programs. You will serve as the primary liaison between Deloitte and our clients, aligning goals, timelines, budgets, and expected outcomes to foster client satisfaction and fuel business growth.

Oversee the successful delivery of data modernization and migration programs, acting as the main client contact. Ensure alignment of project goals, timelines, budgets, and desired outcomes while actively driving client satisfaction and business expansion.

Lead multiple delivery workstreams and data engineering teams. Manage project planning, estimation, and risk mitigation, driving Agile/Scrum methodologies for efficient execution. Conduct architecture, design, and code reviews, ensuring alignment with stakeholders for timely project completion.

Possess 8-11 years of overall IT experience, with a minimum of 5 years focused on GCP Data Engineering projects. Demonstrate proven leadership experience within consulting or enterprise environments.

Exhibit strong hands-on experience with Google Cloud Platform (GCP), including deep expertise in BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Storage. Showcase strong programming skills in Python and SQL, coupled with extensive experience in Spark, distributed data processing, ETL/ELT frameworks, data warehousing, data lake architectures, and real-time streaming solutions.

Experience with CI/CD pipelines, DevOps practices, and Infrastructure as Code (Terraform preferred) is essential. The role also involves designing scalable, cloud-native data platforms, building batch and real-time processing systems, leading data migration initiatives, defining data models and governance frameworks, and ensuring system performance, cost efficiency, and reliability.