Posted 7 days ago
Sr. AI Data Platform Engineer (FinOps Reporting)
@ IT Accel, Inc.
Toronto, Ontario
SIGNALS
Overview
IT Accel, Inc. is hiring a Senior AI Data Platform Engineer for its client to build and maintain FinOps data pipelines covering cloud and technology spend, financial and operational data integration, and trusted reporting datasets. The role is hybrid, with 4 days onsite and 1 remote day.
Responsibilities:
- Build and maintain FinOps data pipelines from ingestion to reporting
- Integrate billing, usage, CMDB, finance and vendor data
- Create trusted datasets and dashboards for cost reporting
- Ensure data quality, lineage, reconciliation and auditability
- Reduce manual reporting through automation
Requirements summary:
- 10 years in data engineering or related data role
- University degree in computer science, engineering, finance or related field
- Strong SQL, data modeling and ETL/ELT experience
- Experience with Databricks, Snowflake, BigQuery or similar platforms
- Experience with Power BI, Tableau, Looker or similar tools
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-03
- First seen by our source
- 2026-11-10
- Source record updated
- 2026-11-10
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Data & analytics |
| Experience | 10+ years |
| Hours per week | 40 (estimated) |
| Travel | air: none · ground: none |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
| Education | Bachelor’s degree (required) |
| Fields of study | Computer Science, Data Engineering, Information Systems, Engineering, Mathematics |
Summary and details written by AI from the posting; the employer’s posting is the authority.
IT Accel, Inc.
| Field | Value |
|---|---|
| Open jobs on Jobsy | 2 |
Job 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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