GCP Data Pipeline Engineer

Must Have Technical / Functional Skills

• 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.

Roles & Responsibilities

Salary Range : $90,000-$110,000 a year

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