What are the responsibilities and job description for the Technical Architect - Data Architecture position at Tephra?
Description :
Location : San Francisco, 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 Technologies :
- 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 Requirements : - Minimum of 10 years of total experience 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
