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Metro locations and known employer increase applicant density despite senior, niche profile.
GCP data architecture and DataOps expertise is highly domain-specific and not easily transferable across industries.
Mandatory 10+ years, GCP certification, and specific DataOps/GCP skills enforce strict filters.
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Lead design and development of scalable data architectures and pipelines on GCP focused on DataOps principles.
Implement and optimize real-time and batch data pipelines integrating diverse data sources with emphasis on automation, version control, and CI/CD practices.
Provide technical leadership, collaborate with cross-functional teams, and maintain documentation adhering to data governance and security policies.
Bachelor's degree in Computer Science, Information Technology, or related field.
Minimum 10 years hands-on experience with GCP data services, data engineering, data pipelines, and DataOps.
Strong proficiency in SQL, Python (or similar programming languages), and experience with version control and CI/CD for data pipelines.
Google Cloud Professional Data Engineer or Data Architect certification required.
Experienced in designing complex data architectures emphasizing Data Products, Data API design, and data ingestion across diverse systems.
Demonstrated ability to lead technical teams and effectively communicate complex concepts to non-technical stakeholders.
Familiar with advanced GCP data services (BigQuery, Dataflow, Dataproc) and able to integrate emerging GCP data technologies into solutions.