Enterprise Data Architect at Applaudo

About You

You are a self-driven professional eager to influence your own development. You seek an organization where you can explore your interests and advance professionally while significantly contributing to the design and evolution of enterprise data ecosystems. You thrive in complex data environments, skillfully combining technical expertise, strategic foresight, and collaborative efforts to craft scalable data architectures that perfectly align with both business and technology requirements.

Competencies You Bring to Applaudo

• Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, or a closely related field is preferred, or equivalent professional experience.
• 6+ years of extensive experience in Data Architecture, Data Engineering, or Enterprise Platform Design.
• Proven strong experience in designing and implementing robust data architecture solutions that meet specific business and technical requirements.
• Profound expertise in data modeling, including Star Schema and Snowflake Schema designs.
• Advanced proficiency in SQL and Python, demonstrated through their application in data architecture and engineering solutions.
• Strong hands-on experience with Spark and dbt for scalable data processing and transformation initiatives.
• Significant experience designing and working with leading cloud data platforms such as Snowflake and BigQuery.
• Extensive experience working with major cloud platforms, including AWS, GCP, and/or Azure.
• Familiarity with metadata management practices and established enterprise data architecture methodologies.
• Experience leading and actively supporting data governance initiatives to ensure data integrity and compliance.
• Strong strategic thinking abilities, capable of translating complex business requirements into scalable and sustainable data solutions.
• Excellent documentation skills, with a focus on creating and maintaining clear, consistent data architecture standards.
• Strong stakeholder management and communication skills, adept at interacting across technical and business teams.
• Fluent English communication skills are essential.

Preferred Certifications

• TOGAF
• AWS Data Architect
• Google Cloud Data Engineer
• CDMP (Certified Data Management Professional)

Responsibilities You Will Be Accountable For

• Define and govern the enterprise data architecture, ensuring that data platforms, models, and data flows are scalable, secure, and perfectly aligned with the overall business strategy.
• Design, develop, and continuously evolve sophisticated data ecosystems that robustly support advanced analytics, artificial intelligence (AI), and critical operational systems.
• Develop and meticulously maintain comprehensive data models, including both Star Schema and Snowflake Schema, to effectively support diverse business and technical requirements.
• Establish and consistently uphold data architecture standards, core principles, and clear guidelines across the entire organization.
• Lead and actively support data governance practices to foster consistent, reliable, and well-managed data assets throughout the enterprise.
• Define and diligently oversee metadata management practices to significantly improve data visibility, consistency, and usability.
• Collaborate effectively with both technical and business stakeholders to translate their specific requirements into highly scalable and robust data architecture solutions.
• Evaluate and recommend optimal data platforms and cutting edge technologies based on their scalability, performance capabilities, and specific business needs.
• Ensure that all data architecture decisions comprehensively support data quality, regulatory compliance, stringent security protocols, and long term maintainability.
• Thoroughly document all aspects of data architecture, including models, standards, and key architectural decisions, to facilitate seamless knowledge sharing and maintain consistency across all teams.
• Provide expert architectural guidance and crucial technical direction to data engineering and other technical teams.
• Support critical cross-functional decision-making processes by ensuring that the data architecture consistently remains aligned with overarching organizational objectives.