Publié il y a 7 jours
Sr. AI Data Platform Engineer (FinOps Reporting)
@ IT Accel, Inc.
Toronto, Ontario
SIGNAUX
Résumé des exigences :
- Our client is seeking a data professional with strong experience in data engineering, analytics engineering, BI engineering, financial data analytics, or a related data-focused role.
- Develop reporting for rightsizing, idle resource cleanup, quota tuning, license reclamation, and other optimization opportunities.
- 10 years of experience in data engineering, analytics engineering, BI engineering, financial data analytics, or a related data-focused role.
- University degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, Finance, Business Analytics, or a related field.
- Strong experience with SQL, data modeling, ETL/ELT pipelines, and large, complex datasets.
- Experience integrating data across multiple enterprise systems.
Source et dates
Type de source non vérifié · apify-linkedin
- Date publiée par l’employeur
- 2026-11-03
- Première détection par notre source
- 2026-11-10
- Mise à jour du dossier source
- 2026-11-10
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
IT Accel, Inc.
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 2 |
Description
Hybrid 4 days onsite, 1 remote.
Position Summary
Our client is seeking a data professional with strong experience in data engineering, analytics engineering, BI engineering, financial data analytics, or a related data-focused role. The position focuses on building and maintaining FinOps data pipelines, integrating technology and financial data across enterprise systems, and developing trusted datasets and analytics to support cost management, reporting, forecasting, optimization, and auditability.
Responsibilities
Position Summary
Our client is seeking a data professional with strong experience in data engineering, analytics engineering, BI engineering, financial data analytics, or a related data-focused role. The position focuses on building and maintaining FinOps data pipelines, integrating technology and financial data across enterprise systems, and developing trusted datasets and analytics to support cost management, reporting, forecasting, optimization, and auditability.
Responsibilities
- Build and maintain FinOps data pipelines from ingestion through reporting and self-service analytics.
- Normalize cloud and technology spend data across provider feeds, including alignment with standards such as FOCUS where applicable.
- 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 datasets for cost reporting by business unit, product, application, environment, owner, platform, and vendor.
- Build analytics to support budget guardrails, variance reporting, anomaly detection, remediation workflows, and optimization tracking.
- Support commitment-based spend analysis, including reservation, savings plan, coverage, utilization, and waste reporting.
- Develop reporting for rightsizing, idle resource cleanup, quota tuning, license reclamation, and other optimization opportunities.
- Build models and dashboards for unit economics, total cost of ownership (TCO), ROI tracking, and business outcome-based cost analysis.
- Partner with Engineering Analytics to deliver enterprise dashboards, automated alerts, recommendations, and workflow automation.
- Ensure data quality, lineage, reconciliation, documentation, controls, and auditability across FinOps data assets.
- Reduce manual reporting and spreadsheet dependency through automation.
- Qualifications
- 10 years of experience in data engineering, analytics engineering, BI engineering, financial data analytics, or a related data-focused role.
- University degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, Finance, Business Analytics, or a related field.
- Strong experience with SQL, data modeling, ETL/ELT pipelines, and large, complex datasets.
- Experience integrating data across multiple enterprise systems.
- Experience with data platforms such as Databricks, Snowflake, BigQuery, Azure Synapse, Microsoft Fabric, SQL Server, or similar tools.
- Experience with BI and reporting tools such as Power BI, Tableau, Looker, or similar platforms.
- Strong understanding of data quality, reconciliation, controls, lineage, documentation, and governance.
Journal
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