Sr. AI Data Platform Engineer (FinOps Reporting)

IT Accel, Inc.; Toronto; Híbrido; CAD 110.000 - 180.000; Jornada completa

Descripción de la vacante

IT Accel, Inc. seeks a senior data professional to design and maintain FinOps data pipelines and analytics, enabling trusted cost datasets and self-service insights. The role emphasizes integration of billing, metadata, CMDB, and vendor data with a focus on cost reporting, forecasting, and auditability.

You will build dashboards and models for unit economics, TCO, and ROI, collaborating with Engineering Analytics to deliver automated insights and alerts in a hybrid onsite/remote environment.

Formación

• 10 years of experience in data engineering, analytics engineering, BI engineering, financial data analytics, or related data-focused role.
• University degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, Finance, Business Analytics, or 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.

Responsabilidades

• 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.

Conocimientos

Data engineering

Analytics engineering

BI engineering

Financial data analytics

Educación

University degree in a relevant field (CS / Data Eng / IS / Eng / Math / Finance / Business Analytics)

Herramientas

Databricks

Snowflake

BigQuery

Azure Synapse

Microsoft Fabric

SQL Server

Power BI

Tableau

Looker

Descripción del empleo

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

• 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.