3 Cloud Dataprep Jobs

As a Data Engineer at our company, you will be responsible for designing scalable and robust AI/ML systems in production, focusing on high-performance and cost-effective solutions. Your expertise in various technologies, including GCP services like BigQuery, Cloud Dataflow, Pub/Sub, Dataproc, and Cloud Storage, along with programming languages such as Python, Java/Scala, and SQL, will be crucial for the success of our projects. Additionally, your experience with data processing tools like Apache Beam, Apache Kafka, and Cloud Dataprep, as well as orchestration tools like Apache Airflow and Terraform, will play a significant role in implementing efficient data pipelines. Knowledge of security protocols such as IAM, Cloud Identity, and Cloud Security Command Center, and containerization technologies like Docker and Kubernetes (GKE) will also be essential in ensuring data integrity and system security. Moreover, your familiarity with machine learning platforms like Google AI Platform, TensorFlow, and AutoML will enable you to develop and deploy cutting-edge AI models. Certification in Google Cloud Data Engineer and Cloud Architect is preferred, demonstrating your commitment to continuous learning and professional growth. In this role, you will collaborate with cross-functional teams, mentor engineers, and provide leadership to ensure that our projects meet business objectives. Your ability to implement MLOps practices, deploy models, monitor performance, and manage version control will be critical for the success of our AI/ML initiatives. Furthermore, your deep understanding of frameworks such as TensorFlow, PyTorch, and Scikit-learn, coupled with experience in data engineering principles, scalable pipelines, and distributed systems like Apache Kafka, Spark, and Kubernetes, will be invaluable assets in designing and deploying advanced machine learning models. The ideal candidate will possess strong leadership and mentorship capabilities, problem-solving skills, project management abilities, and a collaborative mindset. By fostering a positive and productive work environment, you will contribute to the success of our team and the timely delivery of high-quality solutions. At our company, you will have the opportunity to work on cutting-edge projects, collaborate with a highly motivated team, and enjoy a competitive salary, flexible schedule, and a comprehensive benefits package. Join us at Grid Dynamics, a leading provider of technology consulting and engineering services, and be part of our journey to solve complex technical challenges and drive positive business outcomes for our clients worldwide.,

As a GCP Data Engineer in Australia, you will be responsible for leveraging your experience in Google Cloud Platform (GCP) to handle various aspects of data engineering. Your role will involve working on data migration projects from legacy systems such as SQL and Oracle. You will also be designing and building ETL pipelines for data lake and data warehouse solutions on GCP. In this position, your expertise in GCP data and analytics services will be crucial. You will work with tools like Cloud Dataflow, Cloud Dataprep, Apache Beam/Cloud composer, Cloud BigQuery, Cloud Fusion, Cloud PubSub, Cloud storage, and Cloud Functions. Additionally, you will utilize Cloud Native GCP CLI/gsutil for operations and scripting languages like Python and SQL to enhance data processing efficiencies. Furthermore, your experience with data governance practices, metadata management, data masking, and encryption will be essential. You will utilize GCP tools such as Cloud Data Catalog and GCP KMS tools to ensure data security and compliance. Overall, this role requires a strong foundation in GCP technologies and a proactive approach to data engineering challenges in a dynamic environment.,

You should have a strong understanding of the tech stack including GCP Services such as BigQuery, Cloud Dataflow, Pub/Sub, Dataproc, and Cloud Storage. Experience with Data Processing tools like Apache Beam (batch/stream), Apache Kafka, and Cloud Dataprep is crucial. Proficiency in programming languages like Python, Java/Scala, and SQL is required. Your expertise should extend to Orchestration tools like Apache Airflow (Cloud Composer) and Terraform, and Security aspects including IAM, Cloud Identity, and Cloud Security Command Center. Knowledge of Containerization using Docker and Kubernetes (GKE) is essential. Familiarity with Machine Learning platforms such as Google AI Platform, TensorFlow, and AutoML is expected. Candidates with certifications like Google Cloud Data Engineer and Cloud Architect are preferred. You should have a proven track record of designing scalable AI/ML systems in production, focusing on high-performance and cost-effective solutions. Strong experience with cloud platforms (Google Cloud, AWS, Azure) and cloud-native AI/ML services like Vertex AI and SageMaker is important. Your role will involve implementing MLOps practices, including model deployment, monitoring, retraining, and version control. Leadership skills are key to guide teams, mentor engineers, and collaborate effectively with cross-functional teams to achieve business objectives. A deep understanding of frameworks like TensorFlow, PyTorch, and Scikit-learn for designing, training, and deploying models is necessary. Experience with data engineering principles, scalable pipelines, and distributed systems (e.g., Apache Kafka, Spark, Kubernetes) is also required. Nice to have requirements include strong leadership and mentorship capabilities to guide teams towards best practices and high-quality deliverables. Excellent problem-solving skills focusing on designing efficient, high-performance systems are valued. Effective project management abilities are necessary to handle multiple initiatives and ensure timely delivery. Collaboration and teamwork are emphasized to foster a positive and productive work environment.,