Role: Technical Architect - Data Architecture Location Options: Bay Area - CA Responsibilities: - 1. Design Data Architecture: * Develop and design the data architecture framework for the organization. * Create models for databases, data warehouses, data lakes, and other storage solutions to store and manage data in an efficient, scalable, and secure manner. * Establish and maintain the overall data structure and logical/physical designs. 2. Data Governance & Security: * Ensure that data governance policies are followed to maintain data quality, integrity, and consistency. * Implement and enforce data security measures to protect sensitive information and comply with legal and regulatory requirements (e.g., GDPR, CCPA). * Work with compliance teams to ensure data practices meet regulatory standards. 3. Data Integration: * Oversee the integration of data from multiple sources, including internal and external systems, into a unified, efficient data architecture. * Design and implement data pipelines to move data seamlessly between platforms. * Ensure the architecture supports both batch and real-time data processing needs. 4. Collaborate with Stakeholders: * Work closely with Data Engineers, Data Scientists, Business Analysts, and IT teams to understand their data needs and ensure alignment with business objectives. * Gather requirements from business units to ensure the data systems support business operations and decision-making processes. * Provide recommendations for improvements to data storage, management, and analysis based on evolving business needs. 5. Performance & Scalability: * Optimize data systems to improve performance, including fast access to large datasets and quick processing speeds. * Plan for scalability of the data architecture to accommodate future growth in data volume, complexity, and technological advancements. * Evaluate and recommend tools, technologies, and platforms that support efficient data management. 6. Maintain Data Quality & Data Standards: * Establish data standards, including data naming conventions, formats, and definitions. * Ensure data consistency across systems and address issues related to data quality, such as duplication or discrepancies. * Continuously monitor the data architecture and troubleshoot any issues related to data flow, access, or performance. 7. Data Modeling: * Design and implement data models (conceptual, logical, and physical) for enterprise data structures. * Define how data entities relate to one another, ensuring models can be used to meet business requirements and analytical needs. * Create data dictionaries and documentation to ensure transparency and standardization across teams. 8. Data Migration & Transformation: * Lead data migration efforts, particularly during system upgrades or transitions to new platforms. * Define and implement ETL (Extract, Transform, Load) processes for transforming data into usable formats for analytics and reporting. 9. Documentation and Reporting: * Document data architecture designs, processes, and standards for reference and compliance purposes. * Create reports on the status of data architecture projects and provide recommendations to senior leadership. 10. Stay Updated with Data Technol ogies: * Stay current with the latest trends, technologies, and best practices in data architecture, cloud computing, and big data platforms. * Continuously assess new technologies that can improve data architecture and recommend tools for adoption Qualifications: (Please list all required qualifications) Click here to enter text. (Rationalizes basic requirements for candidates to apply. Helps w/rationalization when detailed. Requirements: - 1.Educational Background: Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field. 2.Technical Skills: * Strong expertise in data modeling techniques (conceptual, logical, physical). * Proficiency in SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra). * In-depth knowledge of data warehousing concepts and tools (e.g., Redshift, Snowflake, Google BigQuery) * Experience with big data platforms (e.g., Hadoop, Spark, Kafka). * Familiarity with cloud-based data platforms and services (e.g., AWS, Azure, Google Cloud). * Expertise in ETL tools and processes (e.g., Apache NiFi, Talend, Informatica). * Proficiency in data integration tools and technologies * Familiarity with data visualization and reporting tools (e.g., Tableau, Power BI) is a plus. * Deep understanding of data governance frameworks and best practices. * Knowledge of security protocols, data privacy regulations (e.g., GDPR, CCPA), and how they apply to data architecture 3.Soft Skills: * Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. * Strong problem-solving and critical thinking abilities. * Ability to collaborate across teams and understand business requirements. * Leadership and mentoring skills, particularly when working with junior data engineers or analysts. * Attention to detail and a strong commitment to data quality. 4.Experience: * Extensive experience (5+ years) in data architecture, database management, and data modeling. * Proven track record of successfully designing and implementing data architecture solutions at scale. * Experience working with large-scale data systems, particularly in cloud environments. 5.Preferred Qualifications: * Certification in cloud platforms (e.g., AWS Certified Solutions Architect, Google Cloud Professional Data Engineer). * Experience with machine learning and AI integration into data architectures. * Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). * Experience with advanced analytics and data science use cases. 6.Work Environment: * Collaborative and fast-paced work environment. * Opportunity to work with state-of-the-art technologies. * Supportive and dynamic team culture Salary Range: $171,275 - $254,150 #LI-AD1