Role: Technical Architect - Data Architecture

Location Options: Bay Area - CA

Responsibilities: -

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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: -

• Educational Background:
  
  

Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.

• 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
• 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.
• 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.
• 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.
• 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