This role is ideal for someone with strong technical skills in cloud computing, data engineering, and analytics, who is passionate about working with cutting-edge technologies in GCP to build robust and scalable data solutions.

Key Responsibilities:

Data Architecture and Design:

• Design and implement scalable, reliable, and high-performance data pipelines on Google Cloud.
• Define and implement data architecture strategies to store, process, and analyze large datasets efficiently.
• Create optimized schemas and ensure data structures meet business requirements.

Data Pipeline Development:

• Build and maintain ETL (Extract, Transform, Load) pipelines using tools like Google Cloud Dataflow, Apache Beam, and Cloud Dataproc.
• Work with Google Cloud Storage (GCS), BigQuery, and Pub/Sub for data ingestion, storage, and analysis.
• Automate and orchestrate data workflows using tools such as Apache Airflow or Google Cloud Composer.

Data Processing and Transformation:

• Develop and manage batch and real-time data processing solutions.
• Transform raw data into useful formats for analysis or machine learning models using BigQuery, Dataflow, or Dataproc.

Collaboration:

• Collaborate with data scientists, analysts, and other stakeholders to understand business needs and deliver data solutions.
• Provide data support for machine learning and AI models, ensuring the data is clean, structured, and properly ingested.

Optimization and Monitoring:

• Monitor and optimize the performance of data pipelines, ensuring minimal downtime and efficient use of resources.
• Troubleshoot data issues and resolve bottlenecks in the pipeline or storage systems
  

Required Skills and Qualifications:

• Experience with GCP: Strong experience with GCP services like BigQuery, Google Cloud Storage (GCS), Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Functions.
• Programming Languages: Proficient in languages such as Python, Java, or Scala for developing data pipelines and processing.
• ETL Tools: Experience with ETL frameworks, tools like Apache Beam, Airflow, or Cloud Data Fusion.
• Data Modeling and Warehousing: Understanding of data modeling, relational databases, and data warehousing concepts.
• SQL and NoSQL Databases: Strong proficiency in SQL, with experience in data analysis using BigQuery or other relational databases. Familiarity with NoSQL databases is a plus.
• Cloud Infrastructure: Knowledge of cloud architecture and infrastructure best practices on GCP.
• Data Analytics and BI Tools: Experience working with data visualization tools like Google Data Studio, Tableau, or Looker is a plus.
• DevOps Practices: Experience with CI/CD pipelines, version control systems (e.g., Git), and automated testing.

Preferred Skills:

• Experience with containerized environments, including Docker and Kubernetes.
• Familiarity with machine learning tools like AI Platform on GCP.
• Ability to manage large datasets efficiently and design solutions that scale with growing data volumes.

Education and Certifications:

• A bachelors or masters degree in Computer Science, Information Technology, Data Science, or a related field.
• Google Cloud Certified Professional Data Engineer or other relevant certifications are often preferred.