Delivery Manager – External Data Integration & Vendor Strategy
Location - Toronto, ON (Hybrid)
Role Summary
Our client is a global real estate investor, developer, and manager connects people to exceptional places across the office, logistics, residential, and retail sectors, operating an elite international portfolio with a focus on long-term value creation.
We are seeking a highly skilled Delivery Manager –
External Data Integration to lead the end-to-end lifecycle of sourcing, acquiring, integrating, and operationalizing external data within our Enterprise AI Data Platform (built on Microsoft Fabric and Azure AI Foundry). This role focuses on driving high-impact business use cases by integrating complex external datasets (e.g., market data, macroeconomic indicators, demographic insights, and rental intelligence) from major providers like CoStar, JLL, CBRE, and other external vendors into a centralized Fabric Data Hub.
Operating at the intersection of business strategy, data engineering, and vendor management, you will drive vendor engagement and technical delivery governance. Your goal is to ensure external data is consistently ingested, standardized, and transformed into analytics- and AI-ready data products across the enterprise.
Key Responsibilities
1. External Data Strategy & Requirements
- Partner with business units: Collaborate with Asset Management, Investments, and Research teams to identify external data needs mapped directly to critical business use cases.
- Assess data gaps: Evaluate current data sourcing methodologies and identify gaps in marketplace data availability.
- Formulate acquisition plans: Define the optimal strategic approach (buy vs. build vs. partner) for each required dataset.
- Manage the roadmap: Establish, maintain, and prioritize a rolling roadmap for external data integrations across multi-disciplinary enterprise projects.
2. Vendor Evaluation & Procurement Enablement
- Lead vendor assessments: Evaluate third-party data vendors based on coverage, granularity, update frequency, data quality, and licensing constraints.
- Coordinate commercial reviews: Partner with Procurement on vendor onboarding and commercial pricing, and with Legal on data licensing agreements, usage rights, and restrictions.
- Manage compliance risk: Collaborate with vendor governance teams to ensure compliance with enterprise risk, security, and data privacy policies.
- Structure future-proof contracts: Ensure agreements support enterprise-wide scalability and align with advanced AI and analytics consumption models.
3. Data Integration & Standardization Strategy
- Define ingestion frameworks: Architect end-to-end data ingestion paths into Microsoft Fabric utilizing APIs, automated data feeds, file transfers, and native connectors.
- Harmonize disparate data: Establish transformation rules to standardize multi-vendor datasets with overlapping domains, reconciling differences in data structures, metric definitions, time-series alignment, and geographic hierarchies.
- Govern Medallion layers: Align ingestion pipelines with a strict Medallion architecture (Bronze: raw vendor storage, Silver: cleansed and standardized data, Gold: business-ready, domain-specific semantic models).
4. Delivery Management & Execution
- Lead technical delivery: Act as the primary Delivery Lead across multiple parallel vendor integration tracks, defining project scopes, milestone tracking, and deliverables.
- Orchestrate cross-functional teams: Coordinate tasks between Data Engineers (pipeline development), Data Architects (modeling and standards), and AI/Analytics teams (downstream consumption).
- Ensure operational excellence: Guarantee the deployment of highly reliable pipelines, clean datasets, and robust data lineage documentation.
5. Architecture Definition & Documentation
- Document technical patterns: Map out visual and structural documentation for ingestion frameworks, transformation steps, and cross-domain semantic data models.
- Translate business logic: Convert abstract business requirements into crisp technical specifications for Data Engineering execution.
- Build for scale: Ensure all delivered data integration solutions are reusable, highly scalable, and fully aligned with enterprise Microsoft Fabric and AI Foundry architectures.
6. Data Governance, Quality & Compliance
- Enforce licensing boundaries: Ensure all data engineering pipelines strictly respect legal usage boundaries and vendor licensing data rights.
- Define data quality metrics: Establish data validation rules and implement automated alert workflows to monitor data freshness, data completeness, and overall vendor reliability.
7. Stakeholder Management
- Act as the primary interface: Bridge communication lines between business leaders, technical engineers, third-party vendors, and legal/procurement functions.
- Translate complex concepts: Articulate architecture decisions, technical constraints, and strategic trade-offs to both highly technical and non-technical audiences.
Required Qualifications
Experience
- Industry Experience: 8+ years of professional experience leading data delivery, data program management, integration initiatives, or technology consulting focused on data platforms and advanced analytics.
- Vendor Ecosystems: Proven track record of managing, evaluating, and procuring data from external commercial vendors and managing data contracts.
- Platform Delivery: Demonstrated success driving end-to-end delivery of enterprise data platforms or complex multi-source data integration projects.
Technical Knowledge
- Ingestion Patterns: Deep understanding of modern cloud ingestion methodologies, including REST APIs, webhooks, batch processing, and streaming protocols.
- Architectural Frameworks: Solid knowledge of ETL/ELT pipelines, data warehousing principles, and modern layered data structures (e.g., Medallion architecture).
- Cloud Ecosystems: Direct familiarity with Microsoft Fabric, Azure Data platforms, or equivalent enterprise cloud modern data stack ecosystems (e.g., Databricks, Snowflake).
Business & Leadership Skills
- Commercial Negotiation: Strong capabilities in analyzing data licensing models and partnering with legal/procurement to optimize vendor agreements.
- Financial Stewardship: Ability to critically evaluate data acquisition costs against realized business value to maximize investment ROI.
- Matrix Delivery: Experience driving execution within a cross-functional, highly matrixed, or federated delivery environment.
Nice-to-Have Qualifications
- Domain experience within real estate, investment management, or capital markets data.
- Direct technical experience handling data products from CoStar, JLL, CBRE, or global macroeconomic/demographic data providers.
- Exposure to downstream AI/ML use cases, RAG architectures, semantic models, or building data-as-a-service (DaaS) products.