Requirements
Requirements: Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field. 3+ years of experience in data engineering, with a focus on Google Cloud Platform. Proficient in designing and implementing data pipelines using Google Cloud Dataflow, BigQuery, and related tools. Strong SQL skills and experience in data modeling and data warehousing concepts. Familiarity with programming languages such as Python or Java for data processing tasks. Ability to work with various data sources, including structured and unstructured data. Strong analytical and problem-solving skills with attention to detail. Ability to effectively communicate technical concepts to both technical and non-technical stakeholders. Preferred Qualifications: Google Cloud certifications (e.g., Google Cloud Professional Data Engineer) are highly desirable. Experience with real-time data processing using Apache Beam and Google Cloud Pub/Sub. Understanding of CI/CD practices in data engineering workflows. Knowledge of data visualization tools and techniques to present data insights effectively.
Description
Müller’s Solutions is actively searching for a Google Cloud Data Engineer to enhance our data processing capabilities. In this vital role, you will be responsible for designing and implementing data pipelines leveraging Google Cloud technologies. You will work closely with data analysts, business stakeholders, and other engineering teams to ensure data is accurately processed, integrated, and made available for analysis and reporting. Key Responsibilities:Design, develop, and optimize data pipelines using Google Cloud Dataflow, Pub/Sub, and other GCP data services.Implement data lakes and data warehouses using Google BigQuery, ensuring efficient data storage, retrieval, and processing.Collaborate with data scientists and analysts to understand data requirements and provide solutions that meet business needs.Monitor and evaluate data pipeline performance, implementing necessary optimizations and troubleshooting issues as they arise.Ensure data quality by developing and implementing validation routines and data profiling techniques.Document data architecture, ETL processes, and operational procedures to facilitate knowledge sharing within the team.Stay informed of emerging trends and best practices in data engineering and GCP technologies.Participate in team efforts to define and enhance data governance and management practices.