Position: Data Architect / Engineer
Company: Augment
Category: Data Engineer
Experience required: 5y
English level: fluent

Our **American outsourcing company Augment** is looking for **Data Architect/Engineer** for **US Startup data-centric company** to work fully remote. **English** communication skills are important and should be minimum **B2+ level (upper-intermediate) and higher.**

**Technologies of Interest**:
•	**PostgreSQL/TimeScaleDB 
•	MongoDB 
•	Kafka/RedPanda 
•	Cloud Platforms - Particularly AWS, Google Analytics/BigQuery 
•	Kubernetes 
•	Argo CD 
•	Scripting: Python, *nix shell, SSIS, SQL, YAML 
•	Database Versioning and Deployment Automation: dbmate, sqitch, flyway, liquibase, etc.**

**Requirements**
•	Bachelor’s degree in information technology, computer science, computer engineering, or related fields or equivalent experience
•	Minimum of 5 years of experience as Data Architect
•	Solid understanding of cloud tech, databases, applications, and related technologies
•	Must understand the system development life cycle, project management approaches, and requirements, design, and testing methodologies
•	Demonstrated expertise in SQL development
•	Database administration
•	Data modeling and design
•	Adherence to industry best practices and standards
•	Security focus
•	Must embrace principles and philosophy of GitOps
•	Familiarity with UML

Responsibilities:
•	Translate business requirements into technology requirements and defining data standards and principles
•	Design, and enforce compliance with, the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data
•	Use standard common business vocabulary, participate in developing and delivering on strategic requirements, outline high-level integrated designs to meet those requirements and align with enterprise strategy and related business architecture
•	Translate business requirements into technical specifications for solutions including data streams, lakes, integrations, transformations, stores, and warehouses
•	Define the data architecture framework, standards, and principles, including modeling, metadata, security, reference data, and other data relating to users, customers, vendors, games, and analytics
•	Define how data flows, products generate data, which data is required where, how flows are managed, and how data changes over it’s lifetime
•	Collaborate and coordinate with multiple internal and external stakeholders, partners, vendors, product and data owners to deliver optimal value for stakeholders
•	Design models of data processing that implement the intended business model
•	Produce visual documentation representing key data entities and their relationships
•	Generate a list of components needed to build a designed system
•	Understand established data management and reporting technologies, and have some knowledge of columnar and NoSQL databases, predictive analytics, data visualization, and unstructured data
•	Use cloud-native paradigms to design systems that support elastic scaling, high availability, end-to-end security for data in motion and data at rest, and cost and performance scalability
•	Develop scalable data pipelines that support real-time data streaming and micro-batch data bursts
•	Designed architectures should integrate with legacy slot systems or other data sources through abstraction or careful separation as necessary. They should also be optimized for sharing data across systems, geographies, and organizations
•	Support the ability to deploy automated and active data validation, classification, management, and governance
•	Design architectures to be loosely coupled, enabling services to perform tasks independent of other services.
•	Communicate clearly, simply, and effectively