Sr IT Delivery Manager

Blend is seeking a Senior Delivery Manager to lead complex, high-impact projects that blend AI, data engineering, and cloud technology.

You will own project success from conception through delivery—managing timelines, stakeholder expectations, budgets, and cross-functional teams to transform our clients' most ambitious initiatives into measurable business impact.

This is a role for someone who thrives in technical complexity, communicates with clarity under pressure, and leads with both strategic vision and operational rigor.

Project Lifecycle Leadership: Own scope, schedule, budget, and quality from RFP through delivery.

Financial Stewardship: Track actuals against committed budgets in RFP and SOW; identify and communicate variances to sponsors and stakeholders.

Project Planning & Execution: Build comprehensive project plans, manage Jira boards, Azure DevOps, or Smartsheets; decompose work into clear, trackable stories.

Resource Management: Allocate and optimize team capacity; advocate for the people and skills needed to succeed.

Stakeholder Alignment: Conduct requirements workshops, translate business needs into technical stories, manage expectations, and keep all parties aligned.

Technical Partnership: Work alongside engineers, data scientists, and architects; understand technical feasibility and design trade-offs.

Delivery & Leadership 15+ years of delivery, program, or project management experience (consulting, tech, or product scale preferred).

Proven track record managing multi-million-dollar projects and complex stakeholder landscapes.

Deep expertise in financial management: budget tracking, variance analysis, forecasting, and cost control.

Fluency with Jira, Azure DevOps, Smartsheets, or equivalent planning and tracking tools.

Technical Acumen (Data, AI, & Cloud) Data Engineering Fundamentals: Understand Python, SQL, ETL/ELT pipelines, and data architecture patterns.

Cloud Platforms: Working knowledge of Azure, AWS, GCP; comfort with platform selection and cost optimization.

Data & Analytics Stack: Familiarity with Snowflake, Databricks, and BI tools (Tableau, Power BI).

Data Science & ML: Basic understanding of exploratory data analysis (EDA), modeling workflows, and model deployment.

Gen AI & Agentic AI: Knowledge of LLMs, prompt engineering, token economics, RAG systems, and GPU cost optimization.

Ability to translate technical concepts into business outcomes and validate feasibility with engineering teams.

Leadership & Collaboration Executive Presence: Comfortable presenting to C-suite and managing high-stakes conversations.

Requirements Clarity: Skilled at running discovery workshops, translating ambiguity into actionable stories, and saying 'no' with grace.

Adaptability: Thrive in startup speed or enterprise rigor; comfortable with ambiguity and rapid pivots.

Ownership Mentality: You don't delegate away responsibility; you solve problems with the team.