Technical Program Manager (Data Engineering)

Technical Program Manager (TPM) in AI & Data Practice

As a Technical Program Manager (TPM) in our AI & Data Practice, you will be the bridge between complex client requirements and high-performance engineering execution. You will lead the delivery of sophisticated data platforms and AI-driven solutions—ranging from Snowflake-based data engineering to Generative AI POCs using advanced search and retrieval technologies like Glean.

This is not a "project coordinator" role. We need a hands-on technical leader who understands the "how" of data movement and model implementation, ensuring that our delivery is as architecturally sound as it is on schedule.

Key Responsibilities

• Technical Delivery & Strategy
• End-to-End Program Ownership: Lead the lifecycle of AI and Data projects from initial discovery and Proof of Concept (POC) to full-scale production implementation.
• Cross-Functional Alignment: Act as the primary translator between stakeholders (Clients/Product) and technical teams (Data Engineers/AI Researchers) to ensure technical feasibility matches business value.
• Architectural Fluency: Guide teams through the nuances of Snowflake’s Data Cloud and AI integration patterns (e.g., RAG architecture, vector search, and enterprise search integration with tools like Glean).
• Agile Operations & Execution
• Sprint Management: Own the Jira and Confluence ecosystem. You will translate ambiguous client requests into crisp, actionable epics and stories.
• Lifecycle Governance: Facilitate all agile ceremonies (Standups, Sprint Planning, Retrospectives, and Demos) specifically tailored for the iterative and often unpredictable nature of AI development.
• Dependency Management: Proactively identify and resolve technical blockers, whether they are data access issues, environment misconfigurations, or API limitations.
• Metrics & Reporting
• Data-Driven Visibility: Develop and maintain real-time dashboards to report on program health, velocity, and budget consumption.
• AI-Specific KPIs: Go beyond standard "burndown" charts to track AI-specific success metrics such as model latency, token usage efficiency, and RAG retrieval accuracy.
• Stakeholder Communication: Provide executive-level status reports that distill complex technical risks into clear business impacts.

Technical Profile & Experience

· Experience: 5+ years of experience as a TPM, specifically within Data Engineering, Analytics, or AI/ML domains.

· Data Expertise: Deep understanding of the Modern Data Stack. You should be comfortable discussing data modeling, ETL/ELT pipelines, and cloud data warehousing (specifically Snowflake).

· AI/LLM Knowledge: Familiarity with the implementation of Large Language Models (LLMs), Enterprise Search (Glean), and the infrastructure required to support them (Vector DBs, Orchestration layers).

· Toolkit: Expert-level proficiency in Jira (Advanced Roadmaps, JQL) and Confluence.

· The "Services" Mindset: Proven ability to manage multiple client workstreams simultaneously, maintaining high quality in a fast-paced, billable environment.

Core Competencies

· Ambiguity Navigator: You can take a client's vague "I want to use AI" and turn it into a structured technical roadmap.

· Analytical Rigor: You don't just report metrics; you analyze them to predict slippage before it happens.

· Influence Without Authority: You can lead highly specialized engineers and demanding clients toward a common technical goal.