Architect Data Engineer

About the Role

Day-to-day responsibilities will include collaborating with internal stakeholders to understand business objectives and designing data architectures that meet these needs. The role also involves acting as the technical liaison for clients, managing their expectations, and aligning the architectural roadmap with their business goals.

Quantiphi's commitment to fostering a culture of transparency, diversity, integrity, learning, and growth provides a supportive environment for professionals to thrive. With a backdrop of rapid growth and innovation, this position offers the chance to be part of a team that is shaping the future of AI, ML, and cloud innovation.

What You Will Do

• Design the end-to-end blueprint for a modern data layer that integrates structured, unstructured, and relational (Graph) data for AI agents.
• Define multi-tenant schemas and Knowledge Graph ontologies that enable LLM agents to perform complex reasoning and cross-domain data retrieval.
• Oversee the health, security, and performance optimization of data clusters (Snowflake/Kinetica), ensuring 99.9% availability for mission-critical AI workflows.
• Act as the primary technical liaison for clients, leading discovery workshops, managing technical expectations, and aligning the architectural roadmap with their business objectives.
• Establish benchmarks for data latency and retrieval accuracy, ensuring the data layer can keep pace with the real-time demands of agentic execution.
• Collaborate with internal teams to identify and prioritize data engineering projects based on business needs and technical feasibility.
• Develop and maintain detailed documentation of the data architecture and engineering practices.
• Participate in code reviews and contribute to the improvement of the overall quality of the codebase.
• Stay up-to-date with the latest trends and technologies in data engineering and AI, applying this knowledge to continuously improve Quantiphi's offerings.

What We Are Looking For

• 10+ years of experience in data engineering, with a focus on designing and implementing large-scale data architectures.
• Expertise in AI platforms, including experience with machine learning and deep learning frameworks.
• Strong background in database administration, including experience with Snowflake, Kinetica, and NoSQL databases.
• Proficiency in data modeling and schema design, with the ability to define multi-tenant schemas and Knowledge Graph ontologies.
• Experience with strategic client communication and project management, including leading discovery workshops and managing client expectations.
• Strong understanding of cloud computing platforms, including AWS, Google Cloud, and Azure.
• Experience with data security and compliance, ensuring data privacy and integrity.
• Strong analytical and problem-solving skills, with the ability to analyze complex data systems and identify areas for improvement.

Nice to Have

• Experience with Excel and other data analysis tools.
• Knowledge of agile development methodologies and version control systems such as Git.
• Certification in data engineering or a related field.
• Experience working in a remote environment and collaborating with distributed teams.

Benefits and Perks

• Competitive compensation package.
• Opportunity to work with a leading Applied AI and Big Data company.
• Collaborative and dynamic work environment.
• Professional development opportunities, including training and certification programs.
• Flexible working hours and remote work arrangements.
• Access to the latest technologies and tools in data engineering and AI.
• Recognition and reward for outstanding performance and contributions to the company's growth.
• Comprehensive health and wellness programs, including mental health support and employee assistance programs.

• Tailor your resume and cover letter to highlight your experience in data engineering, AI platforms, and strategic client communication.
• Prepare to discuss specific examples of your data architecture designs and implementations during the interview process.
• Showcase your proficiency in tools like Excel, Snowflake, and NoSQL, and be ready to explain how you've applied these skills in previous roles.
• Emphasize your understanding of the latest trends and technologies in data engineering and AI, and how you stay updated in this rapidly evolving field.
• Be prepared to walk the interviewer through your approach to data security and compliance, and how you ensure data privacy and integrity in your designs.
• Consider including a portfolio or samples of your work, especially if you have experience in designing and implementing large-scale data architectures.