Data & Analytics Team Lead
On behalf of our client in the private sector, PROCOM is looking for a Data & Analytics Team Lead.
The Data & Analytics Team Lead is responsible for delivering highly trusted, timely, and relevant data that enables confident decision-making across Client's real estate enterprise. This role leads the Data & Analytics team, and owns its most foundational and complex responsibility: defining, building, and maintaining the enterprise Data Governance Framework that makes all other data work trustworthy, consistent, and safe.
The Data & Analytics Team Lead is the person accountable for: building the data governance foundation, owns the data platform architecture (Microsoft Fabric, Azure, SQL Server), and drives the data governance programme (data catalog, data discovery, security, lifecycle, quality, and ownership).
Data & Analytics Team Lead Job Description
Team Leadership & Portfolio Management
- Lead and develop the Data & Analytics team — directly managing the Data Engineer and BI Developer, setting clear expectations, providing structured coaching and performance feedback, and building a team culture defined by data quality discipline, continuous improvement, and shared ownership of the portfolio’s outcomes.
- Drive the monthly Service Operations Review for the Data & Insights portfolio — owning operational reporting to senior leadership on data availability, pipeline reliability, data quality incidents, analytics usage, rework volume, and capability maturity progress.
- Manage vendor relationships and external technology partners — including Microsoft (Fabric, Azure, Power BI roadmap engagement), Yardi (data integration and API partnerships), and any third-party data tools or consulting partners — ensuring contracts, SLAs, and roadmap commitments are aligned with Client’s data strategy and cost-to-serve expectations.
Data Governance Framework Design & Programme Ownership
- Design and own Client’s enterprise Data Governance Framework — developing the governing policies, standards, principles, and operating procedures that define how Client’s data is managed, protected, accessed, and used across all systems, portfolios, and business functions; advancing Data Governance maturity.
- Establish data ownership and stewardship accountability — defining data domain ownership across Client’s key business areas (property, financial, leasing, investment, operations), assigning named data owners and stewards, and building the engagement model that makes business leaders active participants in data governance.
- Author and maintain Client’s data governance policies and standards — including data classification policy, data access and authorization policy, data quality standards, data retention and disposal policy, and sensitive data handling standards — in coordination with the Information Security team’s Purview-based data protection programme and Client’s privacy obligations.
- Define and govern the business glossary and shared metric definitions — establishing authoritative, business-agreed definitions for Client’s most critical data concepts and KPIs (occupancy, NOI, portfolio value, lease expiry, etc.) and encoding those definitions in the data catalog and Power BI semantic layer.
Data Catalog, Discovery & Metadata Management
- Own Client’s data catalog programme — selecting, implementing, and operating the data catalog tooling (Microsoft Purview Data Catalog or equivalent) that makes Client’s data assets discoverable, understood, and trusted by both technical teams and business stakeholders, establishing the foundation for informed self-service analytics and responsible AI.
- Drive data asset registration and documentation — systematically cataloguing Client’s priority data assets across source systems (Yardi, SQL Server databases, Azure Fabric), defining asset descriptions, ownership, data lineage, sensitivity classifications, and quality ratings so that data consumers know what data exists, where it comes from, who owns it, and whether it can be trusted.
- Manage the term store, taxonomy, and metadata schema for Client's data environment — designing the metadata standards and tagging conventions that make data searchable and Copilot-ready across Fabric, SharePoint, and Power BI, ensuring AI-generated insights draw from correctly classified, well-described data assets rather than poorly understood or mislabelled content.
- Enable data discovery for business users and analysts.
Data Security, Access Control & Classification
- Own the data security programme for Client’s data and analytics environment — designing and maintaining the access control model for Microsoft Fabric (Lakehouse, Warehouse, and semantic models), SQL Server databases, and Power BI workspaces.
- Work collaboratively with the Senior Manager, Fin-Sec-Ops to define and implement Client’s data classification scheme — categorizing data assets by sensitivity in alignment with Microsoft Purview sensitivity labels and Client’s broader data protection policies, ensuring classification is consistently applied across the Fabric platform, SQL Server, Power BI row-level security, and data catalog entries.
- Manage data access request and approval workflows — designing and operating a formal process for requesting, approving, and provisioning access to data assets.
- Govern sensitive data handling in the data platform — implementing dynamic data masking, column-level security, and row-level security controls in Fabric and SQL Server for datasets.
Data Lifecycle Management & Retention
- Define and govern Clients data lifecycle policy — establishing the rules that determine how long data is retained at each stage (raw ingestion, curated, aggregated, archived) within the Fabric Lakehouse and SQL Server environments.
- Implement data archival and tiering strategies — designing the technical approach for moving historical data between hot, warm, and cold storage tiers within OneLake and Azure Storage, balancing query performance requirements against storage cost targets in alignment with Client’s FinOps practices.
- Manage dataset and report retirement processes — establishing a formal deprecation workflow for legacy SQL Server databases, SSIS packages, SSRS reports, and Power BI datasets that are superseded by Fabric-based equivalents, ensuring nothing is decommissioned without stakeholder notification, migration validation, and documented sign-off.
Data & Analytics Team Lead Mandatory Skills
Education & Experience
- Degree in Computer Science, Information Systems, Data Management, Business Intelligence, or a related field; a master’s degree in a data-related discipline is an asset.
- Minimum 8–12 years of data and analytics experience in an enterprise environment, with at least 3 years in a team lead, data manager, or principal data role with direct people management accountability.
- Demonstrated experience designing and implementing a data governance programme or framework from a low-maturity baseline — not just contributing to an existing programme, but building one.
- Microsoft certifications: DP-600 (Fabric Analytics Engineer Associate), DP-700 (Fabric Data Engineer Associate), PL-300 (Power BI Data Analyst), or DP-203 (Azure Data Engineer Associate). CDMP (Certified Data Management Professional) or equivalent data governance certification is a strong asset.
Data Governance, Catalog & Metadata Management (Primary Expertise Required)
- Data governance frameworks — DAMA-DMBOK or equivalent; data ownership and stewardship models; policy and standards authoring; data quality dimensions and measurement; metric definition governance; and governance council design and facilitation.
- Microsoft Purview Data Catalog — data source scanning and registration, automated lineage, business glossary management, sensitivity label propagation, data access policies, and catalog search and discovery configuration.
- Data classification and security — sensitivity classification schemes, column-level and row-level security in Fabric and SQL Server, dynamic data masking, data access control models (RBAC and ABAC), and integration with Microsoft Purview DLP and Information Protection.
- Data lifecycle and retention — retention policy design, data tiering and archival strategies, decommission workflows, and regulatory retention obligations (PIPEDA, financial and real estate record-keeping requirements).
- Data quality management — quality dimension definition, automated quality monitoring and alerting in Fabric and SQL Server, data quality incident management, and root-cause-driven remediation that addresses quality at the source rather than masking it downstream.
Data Platform, Architecture & Engineering Depth
- Microsoft Fabric — platform architecture (OneLake, Lakehouse, Warehouse, Data Pipeline, Dataflows Gen2, Direct Lake), workspace and capacity governance, medallion architecture design, and the Fabric integration with Power BI and Purview.
- SQL Server and T-SQL — advanced query development, database design (relational and dimensional), stored procedures, views, performance tuning, and SQL Server security model — sufficient to review team output, identify architectural issues, and contribute directly when needed.
- ETL/ELT and data pipeline architecture — SSIS, Azure Data Factory, or Fabric Pipeline patterns; medallion layer design; incremental loading; SCD handling; and pipeline observability and monitoring design.
- Power BI and semantic modelling — certified dataset governance, semantic model design, DAX (advanced measures), row-level security architecture, Power BI Service administration, and deployment pipeline management — sufficient to provide architectural direction to the BI Developer and review semantic model design decisions.
- Azure DevOps — repository and branch governance for data team artefacts, CI/CD pipeline oversight for data deployments, and Azure Boards for portfolio-level delivery tracking and backlog management.
Leadership, Communication & Stakeholder Influence
- People leadership — proven ability to lead, develop, and hold accountable a small high-quality data team; structured performance management; coaching on technical and professional growth; and building a psychologically safe environment where the team takes ownership and escalates risks early.
- Executive communication — ability to translate complex data governance and platform concepts into clear business language for ELT and senior stakeholders; confidence presenting data strategy, maturity roadmaps, and governance programme updates to non-technical audiences.
- Data literacy evangelism — ability to build enthusiasm and shared ownership for data quality and governance across business teams who do not naturally see themselves as data stewards; comfort facilitating governance workshops and making the case for data investment in business-value terms.
- Programme management — ability to manage a multi-workstream data governance programme concurrently with a live delivery portfolio; structured prioritization under resource constraints; and transparent communication of trade-offs to IST leadership when scope, capacity, or timeline pressures arise.
Preferred
- CDMP (Certified Data Management Professional) — Foundation or Practitioner level, anchoring Client’s data governance programme to a recognized international standard (DAMA-DMBOK).
- Yardi Voyager data model experience — deep familiarity with Yardi’s data schema, APIs, and standard reporting views; essential given Client’s Yardi dependency as the primary source of property, leasing, and financial data.
- Microsoft Fabric DP-600 (Fabric Analytics Engineer Associate) — for candidates combining deep governance expertise with Fabric-native engineering depth across Lakehouse, semantic model, and OneLake architecture.
- Real estate or property management industry experience — understanding of property, financial, leasing, investment, and asset management data domains and the reporting cycles, regulatory obligations, and investor reporting requirements that shape Client’s data governance priorities.
- AI and data readiness experience — experience assessing and improving organizational data quality, classification, and governance as a prerequisite for Microsoft 365 Copilot or other LLM-based AI tool deployment; understanding of how data governance quality directly determines AI output trustworthiness.
- Microsoft Graph connectors and external data cataloguing — connecting non-Microsoft source systems (Yardi, ServiceNow, business applications) to Microsoft Purview and Microsoft 365 search, making external data assets discoverable through the same catalog and search surface as internal Microsoft 365 content.
Data & Analytics Team Lead Start Date
ASAP
Data & Analytics Team Lead Assignment Length
12 months to start with possible extensions
Data & Analytics Team Lead Assignment Location
2 days onsite, Vancouver, BC based
