Data Architecture Analyst

at  Darktrace

Founded by mathematicians and cyber defense experts in 2013, Darktrace is a global leader in cyber security AI, delivering complete AI-powered solutions in its mission to free the world of cyber disruption. We protect more than 8,400 customers from the world’s most complex threats, including ransomware, cloud, and SaaS attacks.
Our roots lie deep in innovation. The Darktrace AI Research Centre based in Cambridge, UK, has conducted research establishing new thresholds in cyber security, with technology innovations backed by over 130 patents and pending applications. The company’s European R&D center is located in The Hague, Netherlands.
Headquartered in Cambridge, UK, Darktrace has more than 2,400 employees located globally. Customers include public sector agencies, education institutions, media, organizations supplying critical infrastructure, and businesses of all sizes worldwide.
This is an excellent opportunity to join a fast-growing company, named one of TIME magazine’s “Most Influential Companies” for 2021 and one of Fast Company’s “Most Innovative AI Companies” in 2022. For more information on our cutting-edge technology, visit darktrace.com.
We are seeking a skilled and experienced Data Architect Analyst to join our team. The Data Architect Analyst will be responsible for designing, developing, and implementing data solutions that support our organization’s strategic initiatives. This role involves collaborating with stakeholders to understand business requirements, designing efficient and scalable data architectures, and ensuring data quality and integrity across systems. The ideal candidate will have a strong background in data architecture, database management, and data analysis, along with excellent communication and problem-solving skills.
This role is hybrid with 2 days a week in our London office.

CANDIDATE REQUIREMENTS:

• Proven experience as a Data Architect Analyst, Data Analyst, or similar role in designing and implementing data solutions.
• Proficiency in database technologies such as SQL, NoSQL, and cloud-based data platforms (e.g., AWS, Azure, Google Cloud).
• Strong understanding of data modelling techniques, data warehousing/data lake concepts, data virtualization, and ETL processes.
• Strong understanding of data governance policies, standards, and procedures to ensure data integrity, security, and compliance with regulatory requirements.
• Experience with data visualization tools (e.g., Tableau, Power BI) and analytics languages (e.g., Python, R) preferred.
• Solid understanding of data governance, data security, and regulatory compliance requirements (e.g., GDPR, HIPAA).
• Excellent analytical, problem-solving, and communication skills.
• Possess a keen eye for detail and to be diligent in verifying information and ensuring completeness and accuracy in all deliverables.
• Ability to work effectively both independently and as part of a team in a fast-paced environment.
• Understanding of agile methodologies, DevOps practices, and continuous integration/continuous deployment (CI/CD) processes.
• Certifications such as Certified Data Management Professional (CDMP) or AWS Certified Solutions Architect a plus.

• Data Architecture Design: Design and develop scalable, efficient, and robust data architectures that meet business requirements and support organizational objectives.
• Database Management: Manage databases, including schema design, data modelling, performance tuning, and optimization to ensure optimal performance and reliability.
• Data Integration: Integrate data from various sources and formats, including structured and unstructured data, to provide comprehensive and accurate insights for decision-making.
• Data Quality Assurance: Implement processes and tools to monitor and improve data quality, including data cleansing, validation, and error handling.
• Collaboration: Collaborate with cross-functional teams, including business stakeholders, IT professionals, and data engineers, to understand requirements, prioritize tasks, and deliver solutions effectively.
• Documentation: Document data architectures, data flows, data models, and technical specifications to facilitate understanding, maintenance, and knowledge sharing.
• Emerging Technologies: Stay abreast of emerging technologies, trends, and best practices in data architecture and data management to continuously enhance organizational capabilities.