Sr. AI Data Platform Engineer (FinOps Reporting)
Role Overview
Build and maintain FinOps data pipelines and integrate technology and financial data across enterprise systems.
Develop trusted datasets and analytics for cost management, reporting, forecasting, optimization, and auditability.
The posting requests prior banking client experience and says not to submit Data Scientist profiles.
Key Skills for This Role
Full Job Posting
Position summary
Build and maintain FinOps data pipelines and integrate technology and financial data across enterprise systems.
Develop trusted datasets and analytics for cost management, reporting, forecasting, optimization, and auditability.
The posting requests prior banking client experience and says not to submit Data Scientist profiles.
Location and work arrangement
- Hybrid schedule: four days onsite and one remote in Toronto, Ontario.
- The posting also lists in-person work location; its stated hybrid schedule is used for workplace classification.
Contract and schedule
- Contract duration is six or more months.
- Expected hours are 40 per week.
FinOps data and reporting responsibilities
- Build pipelines from ingestion through reporting and self-service analytics; normalize cloud and technology spend data across providers.
- Integrate billing, usage, tagging, metadata, CMDB, application, owner, finance, contract, and vendor data.
- Support cost taxonomy, allocation rules, metadata standards, showback, chargeback, forecasting, and auditability.
- Create trusted cost datasets and analytics for budgets, variance, anomalies, remediation, optimization, commitments, and waste.
- Develop reporting for rightsizing, idle resources, quota tuning, license reclamation, unit economics, TCO, and ROI.
- Deliver enterprise dashboards, alerts, recommendations, and workflow automation with Engineering Analytics.
- Ensure quality, lineage, reconciliation, documentation, controls, and auditability; reduce manual reporting through automation.
Experience and education
- The overview requests 8–10 years of relevant experience; the qualifications section requests 5–10 years, so the stated ranges conflict.
- Relevant fields include data engineering, analytics engineering, BI engineering, financial data analytics, or related data-focused work.
- A university degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, Finance, Business Analytics, or a related field is requested.
- Prior banking client experience is requested.
Technical qualifications
- Strong SQL, data modeling, ETL/ELT pipelines, and experience with large, complex datasets are requested.
- Experience integrating data across enterprise systems and strong understanding of data quality, reconciliation, controls, lineage, documentation, and governance are requested.
- Experience with Databricks, Snowflake, BigQuery, Azure Synapse, Microsoft Fabric, SQL Server, or similar data platforms is requested.
- Experience with Power BI, Tableau, Looker, or similar BI and reporting tools is requested.
- Must-have skills listed include Python, automation, cloud data environments, deployment and production support, APIs and telemetry ingestion, troubleshooting, data-quality validation, lifecycle understanding, and architecture-level integration understanding.
Compensation
- Pay is $55–$60 per hour.
Posting notes
The posting lists a citizen-only restriction and an AI interview screening process.
An onsite interview may be required.
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