What are the responsibilities and job description for the Director of Data and Engineering position at Objective Partners?
Here is the updated job description with the client name removed and clean round bullet points:
Director, Data Architecture & Engineering
Position Overview
We are seeking an experienced Data Architecture & Engineering professional to join the firm. Reporting to the CFO, this individual will serve as the business’s senior advisor on data platforms, data engineering, data governance, and AI architecture across the investment portfolio.
The role is primarily portfolio-facing and focused on helping the portfolio managers evaluate the existing book, new originations and potential exits to determine the overall impact to the various vehicles which hold the assets. The successful candidate will advise technology and data leaders on modern data architectures, governance models, AI-enablement strategies, and emerging agentic architectures while supporting investment teams on data- and AI-related diligence matters.
This individual will play a foundational role in building a portfolio-wide Data & AI Center of Excellence, developing reusable frameworks, implementation guidance, and best practices that can be leveraged across the portfolio.
Key Responsibilities
Portfolio Data & AI Advisory
Director, Data Architecture & Engineering
Position Overview
We are seeking an experienced Data Architecture & Engineering professional to join the firm. Reporting to the CFO, this individual will serve as the business’s senior advisor on data platforms, data engineering, data governance, and AI architecture across the investment portfolio.
The role is primarily portfolio-facing and focused on helping the portfolio managers evaluate the existing book, new originations and potential exits to determine the overall impact to the various vehicles which hold the assets. The successful candidate will advise technology and data leaders on modern data architectures, governance models, AI-enablement strategies, and emerging agentic architectures while supporting investment teams on data- and AI-related diligence matters.
This individual will play a foundational role in building a portfolio-wide Data & AI Center of Excellence, developing reusable frameworks, implementation guidance, and best practices that can be leveraged across the portfolio.
Key Responsibilities
Portfolio Data & AI Advisory
- Support development of scalable, cloud-native data platforms that enable analytics, AI, and business intelligence capabilities.
- Provide guidance on data engineering operating models, platform ownership, and organizational design.
- Help assess and improve data quality, governance, security, lineage, and accessibility.
- Evaluate AI readiness and identify foundational capabilities required to support enterprise AI adoption.
- Advise on architecture patterns supporting generative AI, intelligent automation, and agentic workflows.
- Support leaders in evaluating major data and AI platform investments.
- Develop practical frameworks for data maturity assessment, AI readiness, and data governance.
- Create reusable reference architectures for modern data platforms and AI-enabled environments.
- Establish perspectives and guidance on:
- Data platform modernization
- Data governance for AI
- Enterprise AI architecture
- Agentic architecture patterns
- Responsible AI implementation
- AI operating infrastructure
- Build and maintain relationships with leading technology providers, cloud platforms, AI vendors, and implementation partners.
- Monitor emerging technologies and translate market developments into actionable guidance for portfolio companies.
- Demonstrated success designing, building, and scaling modern enterprise data platforms.
- Deep expertise in data architecture, data engineering, cloud data infrastructure, governance, and data quality frameworks.
- Strong understanding of enterprise AI architectures, generative AI technologies, agentic systems, and emerging AI platform patterns.
- Experience developing governance frameworks that support scalable and responsible AI adoption.
- Ability to translate complex technical concepts into business-relevant recommendations for executive stakeholders.
- Strong executive presence and ability to engage credibly with CEOs, CTOs, investors, and senior technology leaders.