Position Details: Technical Architect - Data Architecture LSHCERU Industry

Technical Architect - Data Architecture Role: Technical Architect - Data Architecture for Life Sciences, Healthcare, Energy, Resources or Utilities Industry Location Options: San Francisco, CA / New York / New Jersey 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 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. Qualifications: Educational Background: Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field. Industry Experience: Experience working with clients in Life Sciences, Healthcare, Energy, Resources or Utilities Industry. 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. TEPHRA