Data Architect, Data Platform

About The Role

In this opportunity as a Data Architect, Data Platform, you will:

Lead Architecture Design: Spearhead the design and support for the Data Platform, catering to a diverse audience from data engineers to AI & BI users.
Technical Standards and Best Practices: Research and recommend technical standards, ensuring the architecture aligns with overall technology and product strategy. Be hands-on in implementing core components reusable across applications.
Data-Driven Decision-Making: Make quick and effective data-driven decisions, demonstrating strong problem-solving and analytical skills. Align strategies with company goals.
Stakeholder Collaboration: Collaborate closely with external and internal stakeholders, including business teams and product managers. Define roadmaps, understand functional requirements, and lead the team through the end-to-end development process.
Team Collaboration: Work in a collaborative team-oriented environment, sharing information, diverse ideas, and partnering with cross-functional and remote teams.
Quality and Continuous Improvement: Focus on quality, continuous improvement, and technical standards. Keep service focus on reliability, performance, and scalability while adhering to industry best practices.
Technology Advancement: Continuously update yourself with next-generation technology and development tools. Contribute to process development practices.

About You

You're a fit for the role of Data Architect, Data Platform if your background includes:

Educational Background: Bachelor's degree in information technology.
Experience: 8+ years of IT experience with at least 5 years in a lead design or architectural capacity.
Technical Expertise: Broad knowledge and experience with Cloud-native software design, Microservices architecture, Data Warehousing, and proficiency in Snowflake.
Cloud and Data Skills: Experience with building and automating end-to-end analytics pipelines on AWS, familiarity with NoSQL databases.
Data Modeling: Proficient with concepts of data modeling and data development lifecycle.
Leadership: Ability to lead and mentor junior members of the data engineering team, balancing technical decisions with user needs and business constraints.
Programming Skills: Strong programming skills in languages such as Python or Java.
Regulatory Awareness: Familiarity with data governance, data security, and privacy regulations.
Containerization and Orchestration: Experience with containerization technologies like Docker and orchestration tools like Kubernetes.