Gcp Data Engineer

Job Title: Google Data Engineer / Lead

Experience: 5-10 years

Job Summary: An experienced Google Data Engineer / Lead to design, develop, and maintain large-scale data processing systems on Google Cloud Platform (GCP). The ideal candidate should have a strong background in data engineering, data warehousing, and cloud-based data platforms.

Responsibilities:

• Data Pipeline Development: Design, develop, and maintain data pipelines using Google Cloud Dataflow, Cloud Composer, and BigQuery.
• Data Warehousing: Work with data architects and business stakeholders to design and implement data warehousing solutions using BigQuery and Cloud Storage.
• Data Governance: Develop and maintain data governance policies, procedures, and standards for GCP.
• Collaboration: Work with cross-functional teams to identify and prioritize project requirements, provide technical guidance, and ensure data quality.
• Cost Optimization: Optimize GCP costs and ensure data processing efficiency.

Required Skills:

• Google Cloud Platform: In-depth knowledge of GCP, including Cloud Dataflow, BigQuery, Cloud Storage, and Cloud Composer.
• Data Engineering: Strong understanding of data engineering concepts, including data modeling, ETL, and data warehousing.
• Expertise in Cloud storage, Dataproc, Cloud Functions, Messaging/Streaming (pub-sub)
• Programming: Proficiency in programming languages, including Python, Java, or SQL.
• Data Governance: Familiarity with data governance frameworks, including data quality, data security, and data compliance.
• Communication: Excellent communication and collaboration skills.

Preferred Skills:

• Google Cloud Certifications: Relevant certifications, such as Google Cloud Certified - Professional Data Engineer or Google Cloud Certified - Enterprise Data Engineer.
• Big Data: Experience with big data technologies, including Hadoop, Spark, or NoSQL databases.
• Machine Learning: Experience with machine learning frameworks, including TensorFlow or Scikit-learn.
• Cloud Experience*: Experience with other cloud-based data platforms, including AWS or Azure