Gcp Data Engineer
2 weeks ago
Role & responsibilities:
Data Pipeline Development: Design, build, and maintain data pipelines for data ingestion, processing, and transformation. This involves using GCP services like Cloud Dataflow, Pub/Sub, and Cloud Storage to handle large volumes of data efficiently.
Data Architecture & Modeling: Architect data systems and data stores, including modernizing data lakes and data warehouses on GCP. You will be responsible for creating and optimizing data models to ensure data is clean, organized, and ready for analysis.
ETL/ELT Processes: Develop and maintain ETL (Extract, Transform, Load) and ELT workflows to automate data flows from various sources.
Performance & Optimization: Monitor and optimize the performance of data processing systems and queries to improve efficiency and reduce costs.
Collaboration: Work with cross-functional teams, including data scientists and business analysts, to understand their data requirements and provide data solutions that meet business needs.
Data Quality & Security: Ensure data quality, integrity, and security by implementing governance practices, access controls, and security protocols.
Required Skills & Qualifications:
Technical Expertise: Proven hands-on experience as a Data Engineer with a strong focus on GCP.
Core GCP Services: Proficiency with key GCP data services like BigQuery, Cloud Dataflow, Cloud Storage, and Cloud Pub/Sub.
Programming: Strong programming skills in languages such as Python or Java.
Database & SQL: Expertise in SQL and a solid understanding of data warehousing, data modeling, and database management.
Big Data Frameworks: Experience with big data technologies like Apache Beam, Apache Spark, or Hadoop is highly desirable.
Certification: A Google Cloud Professional Data Engineer certification is often a preferred qualification.
Problem-Solving: Excellent analytical and problem-solving abilities to troubleshoot and resolve data-related issues.
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