Position: Data Architect
Company: Svitla Systems
Category: Data Science
Experience required: 5y
English level: upper

**Requirements: **
Bachelor's Degree in Computer Engineering or a related field required, or equivalent experience education.
Experience with data architecture standardization and enterprise-level data governance and quality initiatives.
Expertise in data analysis, modeling, design, implementation, capacity planning, and tuning.
Expertise in modern cloud data management and analysis technologies.
Expertise in software development, best practices, and life cycle management.
Experience with NoSQL systems (e.g. MongoDB, DynamoDB).
Knowledge of SQL.
Familiarity with microservices.
Experience with AWS.
Analytical mindset, critical thinker, and problem-solver.

**Responsibilities:**
Be autonomous, self-driven, and capable of navigating and leading through ambiguity.
Be able to articulate complex topics to non-technical audiences.
Pursue business value with an automation-first approach to problem-solving.
Develop scalable, resilient, and highly-available services.
Design and lead implementation of data as a product technology stack.
Unify data platform architecture drives the product and engineering innovation through the design, implementation, and maintenance of data infrastructure as a platform.
Lead and develop data architecture, modeling, pipelining, governance, security, and best practices.
Effectively communicate and work with cross-functional teams of systems architects, technical leads, engineers, and product managers to bring company and product vision to life agile, effective, and efficient.
Lead and support ETL development.
Carry out source to target data mappings.
Participate in product discovery and lead product solution phases.
Carry out proof of concept and prototypes data solutions.
Work with delivery teams to provide consultation governance on data architecture and integration and contribute to delivering projects.
Contribute to developing and publishing data management standards, principles, and policies.
Work with other data practitioners in the development of enterprise data services.
Provide leadership to ensure good data quality is intact and maintained when developing data solutions.
Participate in the resolution of data quality and data integration issues.