GCP Data Pipeline Engineer

Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation. Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch. Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection. Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark. Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server). Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, GCP, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus. MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker). Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling.