Data Architect

• Design, architect, and implement scalable data solutions on Google Cloud Platform (GCP) to meet the strategic data needs of the organization.
• Lead the integration of diverse data sources into a unified data platform, ensuring seamless data flow and accessibility across the organization.
• Develop and enforce robust data governance, security, and compliance frameworks tailored to GCP's architecture. Collaborate with cross-functional teams, including data engineers, data scientists, and business stakeholders, to translate business requirements into technical data solutions.
• Optimize data storage, processing, and analytics solutions using GCP services such as Big Query, Dataflow, and Cloud Storage.
• Drive the adoption of best practices in data architecture and cloud computing to enhance the performance, reliability, and scalability of data solutions.
• Conduct regular reviews and audits of the data architecture to ensure alignment with evolving business goals and technology advancements.
• Stay informed about emerging GCP technologies and industry trends to continuously improve data solutions and drive innovation.

Profile Description:

• Experience: 12-15 years of experience in data architecture, with extensive expertise in Google Cloud Platform (GCP).
• Skills: Deep understanding of GCP services including BigQuery, Dataflow, Pub/Sub, Cloud Storage, and IAM. Proficiency in data modeling, ETL processes, and data warehousing.
• Qualifications: Masters degree in Computer Science, Data Engineering, or a related field.
• Competencies: Strong leadership abilities, with a proven track record of managing large-scale data projects.
• Ability to balance technical and business needs in designing data solutions. Certifications: Google Cloud Professional Data Engineer or Professional Cloud Architect certification preferred.
• Knowledge: Extensive knowledge of data governance, security best practices, and compliance in cloud environments.
• Familiarity with big data technologies like Apache Hadoop and Spark.
• Soft Skills: Excellent communication skills to work effectively with both technical teams and business stakeholders.
• Ability to lead and mentor a team of data engineers and architects. Tools: Experience with version control (Git), CI/CD pipelines, and automation tools. Proficient in SQL, Python, and data visualization tools like Looker or Power BI.

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