Join Capgemini Invent as a GCP Data Engineer and drive transformative solutions for our clients. We are seeking inventive transformation consultants who blend strategic, creative, and scientific capabilities to deliver cutting-edge, data-informed solutions. If you are passionate about leveraging technology for impact, this role offers a unique opportunity to contribute to a global leader in business and technology transformation.

Our environment champions your career growth with diverse programs and opportunities for exploration. We also support flexible work arrangements, including remote work and flexible hours, to foster a healthy work-life balance. Become part of a responsible and diverse global team dedicated to accelerating the dual transition to digital and sustainable futures.

In this role, you will collaborate closely with data scientists, analysts, and application developers to construct high-quality data solutions. Key responsibilities include ensuring robust data quality, governance, and security across all data pipelines and storage layers. You will develop and optimize efficient code in Python and PySpark, alongside advanced SQL queries for data extraction and transformation. Building processes for data transformation, structure, metadata, dependency, and workload management is crucial. Furthermore, you'll implement data quality checks and monitoring to guarantee data accuracy and reliability.

We are looking for candidates with a strong analytical foundation for working with structured, semi-structured, and unstructured datasets. Essential expertise includes working knowledge of message queuing, stream processing, and highly scalable big data stores, specifically within GCP services like Dataflow, BigQuery, Pub/Sub, Cloud Composer, and Cloud Storage. Hands-on experience with CI/CD tools and DevOps practices is required. Proficiency in Python, PySpark, and strong SQL skills, including optimization and performance tuning, are mandatory. Familiarity with data warehousing, data lake concepts, ETL processes, and experience with big data tools such as Hadoop, Spark, and Kafka are also necessary. Experience with both relational (Microsoft SQL Server, MySQL, PostgreSQL, Oracle) and NoSQL databases (Hadoop, Cassandra, MongoDB) is expected.