GCP Data Architect SAN Engineering Solutions

About SAN Engineering Solutions

Job Description

Job Overview We are seeking a skilled Data Consultant/ Data Architect to design and manage the enterprise data architecture, ensuring the efficient and secure handling of data across the organization. The ideal candidate will possess extensive experience in data modelling, database design, and data management practices. Responsibilities Data Architecture Design: Drive architecture and design conversations with client Architecture Board Develop and maintain the data architecture, including data models (industry standard Telecom data model), data flow diagrams, and database schemas. Design and implement data integration frameworks, ensuring data consistency and reliability. Be familiar with Data modelling concepts (SCD's, normalisation, star) Experienced with Data product (data mesh) design Experienced in design & delivery of large scale Data Analytics platforms on Google Cloud (BigQuery, dataflow, cloud storage, dataproc, composer, cloud function) Has understanding of FinOps concepts Establish and enforce data governance policies and procedures. Ensure compliance with data privacy regulations and industry standards Define and implement data strategy aligned with organizational goals. Collaborate with stakeholders to understand data requirements and translate them into effective data solutions. Design and manage data pipeline processes for historic data migration and data integration. Ensure the accuracy and integrity of data throughout the ETL process. Implement and manage big data solutions, including data lakes and data warehouses. Work closely with Stakeholders, analysts, and other stakeholders to understand data needs. Communicate data architecture strategies and solutions to technical and non-technical audiences. Work with and communicate effectively with business and IT stakeholders/SMEs to understand context behind tickets Document according to given standards, Stay updated with emerging data technologies and industry trends. Continuously improve data architecture and data management practices.,