Data Integrity Lead

SUMMARY

The Data Integrity Lead is the Quality Assurance (QA) and Data Governance owner for the integrity and assurance of all operational and planning data used across the CIBC Integrated Facilities Management (IFM) portfolio. The role is accountable for ensuring that data is accurate, complete, consistent, and decision‑ready, supporting effective governance, reporting, performance management, compliance, and risk reduction.

This position applies BGIS’s established data quality and governance framework and embeds data validation, controls, and automated quality checks directly into frontline and back‑office workflows. In addition to strong governance and assurance accountability, the role brings hands‑on technical capability, using scripting and automation to identify defects, enforce standards, and reduce manual quality checks.

The Data Integrity Lead partners closely with Facilities Management, Project Delivery, Capital Planning, Lease Administration, Finance, and Portfolio teams to ensure that data structures, terminology, and validation requirements are consistently applied across the account. The role operates as a client‑facing governance and assurance function, providing independent data quality oversight while enabling automation and future AI‑driven controls.

KEY DUTIES & RESPONSIBILITIES

Data Governance & Quality Assurance

- Act as the Data Quality Assurance (QA) owner for all operational and planning data across the CIBC IFM portfolio.
- Apply BGIS’s established data quality and governance standards, ensuring consistent implementation across systems and workflows (this role does not design enterprise frameworks).
- Define, implement, and monitor data validation checkpoints, ownership rules, and exception‑handling processes across service lines.
- Conduct data readiness and quality assessments to support governance forums, KPI reporting, audits, and executive decision‑making.
- Proactively identify and prevent data defects (e.g., duplicate assets, inaccurate location data, mis‑scoped projects, incomplete or inconsistent fields) before they enter FM, Project, or Capital Planning workflows.
- Produce data quality scorecards, defect trend analysis, and remediation recommendations.

Technical Data Quality Automation & Controls

- Design, develop, and maintain automated data quality checks, validation scripts, and exception reports to reduce manual QA effort and improve consistency.
- Use technical tools and scripting (e.g., SQL, Python, PowerShell, or similar) to interrogate datasets, automate recurring quality checks, and flag anomalies.
- Build lightweight automation to enforce required fields, validate hierarchies, and ensure compliance with data standards at key ingestion and handoff points.
- Partner with BI, IT, and system owners to prototype and operationalize automated controls, without owning platforms or analytics solutions.
- Document automation logic, validation rules, and controls to ensure transparency, repeatability, and auditability.

Cross‑Functional Integration & Enablement

- Work closely with Facilities Management, Project Delivery, Capital Planning, Lease Administration, Workplace, and Finance teams to embed data quality expectations into operational and planning processes.
- Align terminology, data definitions, and required data attributes across service lines (e.g., location hierarchies, asset identifiers, project classifications).
- Support teams in understanding upstream and downstream data dependencies and their impact on reporting, planning, and governance outcomes.
- Act as a facilitator and advisor to promote shared accountability for data integrity across the account.

Performance, Reporting & Governance Support

- Provide data assurance for KPI packages, dashboards, governance decks, and audit‑ready reporting.
- Validate the integrity of data feeding operational, financial, and performance reporting, ensuring outputs are defensible and traceable.
- Identify and communicate data‑related risks that may affect decision quality or governance outcomes, with recommended corrective actions.
- Support governance, compliance, and performance management processes through reliable, decision‑ready data.

Continuous Improvement & Digital Adoption

- Embed data quality controls, automation, and validation logic into FM, Project, and Capital Planning workflows to strengthen “quality at source.”
- Partner with field and back‑office teams to address data quality issues at the point of capture, recognizing that desktop cleanup alone is insufficient.
- Identify opportunities to simplify data entry, reduce rework, and improve adoption of digital tools through automation and clearer standards.
- Contribute to the evolution of data quality and governance practices in support of future AI‑enabled and automated controls.

KNOWLEDGE & SKILLS

- University degree in Information Management, Engineering, Analytics, Computer Science, or a related discipline.
- 5+ years of experience working in data governance, data quality, data controls, or operational data management in an enterprise environment.
- Strong understanding of data quality principles including accuracy, completeness, consistency, timeliness, validity, and traceability, and how to enforce them through controls and validation logic.
- Hands‑on experience using technical tools and scripting (e.g., SQL, Python, PowerShell, or equivalent) to interrogate datasets, automate data quality checks, and produce exception reporting.
- Experience developing automated data validation rules, quality checks, and defect detection logic at data ingestion and handoff points.
- Working knowledge of enterprise operational systems and data flows (e.g., CMMS, project delivery systems, capital planning tools) and how upstream data quality impacts downstream reporting and decision‑making.
- Familiarity with data management concepts such as metadata, master data, data lineage, and exception handling, with the ability to apply them in practical, operational contexts.
- Experience collaborating with BI, analytics, and IT teams to support data quality and control automation, without assuming responsibility for data architecture, platforms, or dashboard development.
- Ability to analyze complex data issues, identify root causes, and design effective remediation approaches that prevent recurrence rather than relying on manual cleanup.
- Strong understanding of governance, audit readiness, compliance, and risk management requirements as they relate to data integrity and reporting defensibility.
- Experience embedding data quality expectations into business processes and workflows, including field and operational activities.
- Commitment to continuous improvement, including the use of automation and emerging technologies to reduce manual QA effort and support future AI‑enabled data quality controls.
- Strong verbal and written communication skills, with the ability to explain data issues, controls, and risks to both technical and non‑technical stakeholders.
- Highly organized, detail‑oriented, and capable of operating independently while influencing cross‑functional teams without direct authority.

Licenses and/or Professional Accreditation

- Certification or formal training in Data Governance, Data Quality, or Information Management considered an asset (e.g., CDMP, DGSP, DAMA‑aligned certifications).
- Professional certification in analytics, data management, or automation tools (e.g., SQL, Python, data engineering or process automation certifications) considered an asset.
- Certification or demonstrated knowledge of governance, risk, or quality management frameworks (e.g., ISO‑aligned quality management, audit, or compliance training) considered an asset.
- Certification related to process improvement or controls (e.g., Lean, Six Sigma, or similar) considered an asset.
- Facilities, real estate, or capital‑program related certifications are not required but considered beneficial where they support understanding of operational data environments.

This is a regular, full-time position with a salary range of $96,322 - $120,402 per annum. The starting salary will be based on the successful candidate’s competencies, including but not limited to experience, education and performance related to this role.

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