Director of Data Engineering

Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders.

Consulting and private equity firms come to proSapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge.

We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.

Data is the foundation of what we do — and we're rebuilding ours to power the next generation of intelligent, agent-driven systems.

We're looking for a Director of Data Engineering to lead this transformation. This isn't a role about keeping infrastructure running; it's about reimagining what our data platform can do. Working closely with our CTO, you'll shape our build-vs-buy strategy, forge the right external partnerships, and grow the in-house capabilities needed to compete in an increasingly automated, AI-driven landscape.

You'll lead a talented, multidisciplinary team of data and AI engineers, with a mission to make our proprietary data richer, more searchable, and ready for high-velocity, real-world use cases. That means designing unified search across vector, keyword, and graph approaches; building multi-agent and human-in-the-loop workflows; developing a robust knowledge graph; and architecting a cross-cloud orchestration strategy that scales.

The key duties of this role will include:

• Strategic Collaboration: Partner directly with the CTO to orchestrate the Build + Buy technical vision. You will lead the Partner + Upskill talent strategies, identifying when to leverage external tech titans/startups and how to mentor the internal team to adopt emerging capabilities.

• AI Search: Oversee the team’s design, deployment and tuning of a unified search layer that combines vector, keyword, and GraphRAG search. You will be responsible for the architecture + engineers who are building agents to navigate complex relational data with high precision.

• Agentic Architecture: Architect cloud-native patterns for multi-agent and human-in-the-loop workflows. You will implement the latest frameworks to ensure AI agents can perform complex, iterative tasks with human oversight.

• Knowledge Graph & Data Enrichment: Direct the engineering of automated pipelines that extract high-fidelity insights from unstructured sources to build a Universal Knowledge Graph, creating a proprietary advantage through enriched data.

• Cross-Cloud Data Orchestration: Lead engineering efforts, in close partnership with the Director of Engineering Platform Services & Apps, to work seamlessly across cloud environments, ensuring data fluidly moves from source to intelligence layers without being bottlenecked by specific cloud provider constraints.

• Engineering Focus & Efficiency: Act as a force multiplier for the team; ensure engineers are exclusively applying their energy to high-value problems requiring our specific domain knowledge. Constantly evaluate emerging approaches that tech titans and other startups can solve for us to ensure we don't "reinvent the wheel."

• Continuous Improvement: Foster a culture of high velocity by leveraging AI-assisted coding and modern agile practices, partnering closely with our Product team, while proactively paying down technical debt through smart talent allocation.

• Cloud-Native Leadership: Solid experience overseeing teams that have a remit to learn and use scalable data architectures across major public cloud providers, particularly AWS and GCP.

• Agentic Systems: Proven experience architecting multi-agent systems and managing the orchestration of autonomous AI workflows.

• AI Search Expertise: Solid experience overseeing teams that have a remit to learn and use GraphRAG, Vector Databases, and hybrid search implementations.

• Data Modeling & Graphs: Experience leading teams in the design and scaling of taxonomies, ontologies and ideally Graph-based data structures to map complex, interconnected entities.

• AI/MLOps Oversight: Experience directing the deployment of Large Language Models (LLMs) in production, focusing on RAG pipelines, human-in-the-loop interfaces, and model evaluation.

• Strategic Sourcing & Talent Growth: A track record of mentoring teams through technological shifts and effectively evaluating third-party AI offerings for integration.