Goods & Services - Senior Data Architect
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Requirements
• Bachelor’s or master’s degree in computer science, engineering, or related field. • 5 years’ experience in data architecture, database design, and data management. • Experience in data modeling, database technologies, data warehousing, and ingestion frameworks. • Experience with NoSQL, Kafka, Middleware, and cloud-based platforms (e.g., AWS, Snowflake) • Experience designing and implementing callable, high-performance, and resilient data architectures for complex enterprise environments. • Technical knowledge in data quality standards, governance and design. • Excellent leadership, communication, and interpersonal skills, with the ability to effectively collaborate with senior technology leaders at all levels of the organization. • Demonstrated ability to think strategically, drive results, and navigate complex technical and organization challenges in a fast-paced, dynamic environment. • Certifications such as Certified Solutions Architect.
Responsibilities
• Design, implement, and optimize data models, databases, data warehouses, and data lakes to support data storage, retrieval, and analysis needs. • Support strategic vision and roadmap for data architecture, in alignment with the company’s overall objectives and goals. • Define data architecture standards, best practices, and patterns to ensure the integrity, reliability, and scalability of our data assets. • Collaborate with cross-function teams, including software engineering, data science, and business domains to align data architecture initiatives with business requirements and technical objectives. • Architect and implement data integration solutions, ETL pipelines, and data governance frameworks to ensure the accuracy, consistency, and security of data across all systems and processes. • Design and implement master data management (MDM) strategies, data quality management processes, and metadata management solutions to enable data governance and compliance. • Evaluate and recommend data technologies, tools, and platforms to optimize performance, cost, and scalability. • Build strong partnership with data engineering to ensure proper alignment fostering a culture of innovation, collaboration, and continuous learning.
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