BI Analytics Engineer

Hydro One Inc.; Toronto; Hybrid; CAD 90,000 - 140,000; Full time

Job summary

Hydro One Inc. is seeking a BI / Analytics Engineer to support data, semantic models, and reporting for contact center analytics built on Microsoft Fabric and Power BI.

The role focuses on transforming curated data into trusted analytical models, sustaining a centralized Power BI semantic model, and ensuring accuracy and performance across multiple business reports. Hybrid work, Toronto office presence required.

Qualifications

• 3+ years of hands-on experience developing and supporting Power BI solutions in a production environment.
• Experience designing, maintaining, and troubleshooting shared or centralized Power BI semantic models supporting multiple reports.
• Hands-on experience with Microsoft Fabric and Direct Lake semantic models.
• Advanced DAX skills, including filter context, relationship behaviour, virtual tables, time intelligence, reusable measures, and performance-aware calculation design.
• Strong SQL skills for data profiling, reconciliation, root cause analysis, and validation.
• Experience working with Fabric Lakehouses and Delta tables, including validating data between Lakehouse tables and semantic model outputs.
• Experience resolving production incidents involving missing data, unexpected totals, filter behaviour, model changes, or report performance.
• Experience assessing model dependencies and regression impacts before deploying changes.
• Ability to document technical findings clearly and work independently with business and technical teams.

Responsibilities

• Maintain and enhance centralized Power BI semantic models used by multiple downstream reports.
• Design and manage fact and dimension tables, relationships, hierarchies, perspectives, display folders, formatting, metadata, and model organization.
• Develop, test, document, and optimize reusable DAX measures and shared KPI logic.
• Assess downstream impacts before changing tables, columns, relationships, measures, or calculation logic.
• Maintain consistent metric definitions and business rules across reports connected to the shared model.
• Identify unused or duplicative model objects and support controlled model cleanup.
• Use model lineage and dependency analysis to understand how semantic model changes affect reports and calculations.
• Provide knowledge transfer and practical support to report developers and analysts using the centralized model.

Skills

Power BI

Fabric

Direct Lake

DAX

SQL

Data profiling

Data reconciliation

Validation

Documentation

Independent work

Tools

Microsoft Fabric

DAX Studio

Tabular Editor

Performance Analyzer

XMLA endpoints

Git integration

Azure DevOps

Job description

We are seeking a BI / Analytics Engineer to support the data, semantic model, and reporting components of contact center analytics solutions built on Microsoft Fabric and Power BI.

This role is responsible for transforming curated data into trusted, reusable analytical models and for the day-to-day sustainment and enhancement of a centralized Power BI semantic model supporting multiple business reports. The successful candidate will work directly with Gold Lakehouse tables, Direct Lake semantic models, DAX measures, relationships, metadata, and downstream Power BI reports to ensure reporting data is accurate, consistent, performant, and supportable.

The role will investigate production incidents and data discrepancies across the analytical reporting path, from curated Fabric data through the semantic model and report layer. It will implement semantic model changes, reusable metrics, minor upstream transformations where appropriate, report enhancements, and new reports for business users. The role works closely with data engineers, business subject matter experts, and technical support teams, but remains a hands-on technical delivery position.

Key Responsibilities

• Maintain and enhance centralized Power BI semantic models used by multiple downstream reports.
• Design and manage fact and dimension tables, relationships, hierarchies, perspectives, display folders, formatting, metadata, and model organization.
• Develop, test, document, and optimize reusable DAX measures and shared KPI logic.
• Assess downstream impacts before changing tables, columns, relationships, measures, or calculation logic.
• Maintain consistent metric definitions and business rules across reports connected to the shared model.
• Identify unused or duplicative model objects and support controlled model cleanup.
• Use model lineage and dependency analysis to understand how semantic model changes affect reports and calculations.

Microsoft Fabric & Direct Lake Support

• Support semantic models operating in Direct Lake mode against Fabric Lakehouse Delta tables.
• Troubleshoot Direct Lake issues involving data availability, framing, model metadata, table or column changes, relationships, partitions, and report query behaviour.
• Validate that non-production and production Lakehouse schemas remain compatible with semantic model deployments.
• Work with Fabric Lakehouses, SQL analytics endpoints, notebooks, deployment pipelines, workspaces, and Power BI Apps as required for reporting delivery.
• Distinguish between source-data freshness, pipeline completion, semantic model state, and report-layer behaviour during incident investigation.
• Participate in performance analysis involving table size, cardinality, query patterns, DAX complexity, concurrency, and Fabric capacity consumption.
• Analyze curated data to understand grain, keys, relationships, duplicates, nulls, late-arriving records, and source-system exceptions.
• Validate fact and dimension data across Fabric Lakehouse tables, the semantic model, DAX query results, and Power BI report outputs.
• Reconcile business metrics against source-system or operational results and explain confirmed differences.
• Determine whether reported issues originate in source data, Silver-to-Gold transformation logic, the semantic model, DAX, filters, or report configuration.
• Write SQL, DAX queries, and notebook-based validation code to investigate incidents and verify changes.
• Partner with data engineers when fixes require changes to Gold tables, aggregation tables, notebooks, or upstream transformation logic.

Reporting Sustainment & Incident Resolution

• Respond to P2-P4 IT incidents and user-reported issues related to data, semantic models, Power BI reports, calculations, performance, and missing or incomplete results.
• Perform structured root cause analysis and document evidence, findings, resolution, workarounds, and escalation paths.
• Reproduce issues using defined filters and time periods, validate expected logic, and confirm the resolution with business users.
• Escalate source-system, pipeline, platform, access, or capacity issues to the appropriate technical team with clear diagnostic evidence.
• Maintain technical runbooks, known-issue documentation, model documentation, and incident investigation procedures.

Report Enhancements & New Development

• Develop and maintain Power BI reports that consume the centralized semantic model.
• Implement new measures, filters, visuals, drill-through paths, tooltips, report pages, and usability improvements.
• Build new reports using shared, governed model objects where possible rather than duplicating business logic in individual reports.
• Translate approved business requirements into data mappings, semantic model changes, DAX calculations, and report functionality.
• Complete developer testing, data reconciliation, regression testing, deployment, and post-deployment validation.
• Promote report and semantic model changes through established non-production and production deployment processes.

Performance & Model Optimization

• Analyze semantic model and report performance using query behaviour, model metadata, table volumes, relationships, and DAX execution patterns.
• Recommend and implement model improvements such as star-schema alignment, simplified relationships, reduced cardinality, removal of unused columns, reusable measures, and appropriate aggregations.
• Identify calculations that should remain in DAX versus those better implemented in curated Gold or aggregate tables.
• Validate that optimizations preserve metric accuracy and do not introduce regressions across connected reports.
• Support monitoring of data freshness, report responsiveness, and recurring failure patterns.
• Work with business stakeholders to understand data questions, confirm expected calculations, and validate report changes.
• Collaborate with data engineers on transformation logic, table design, data-quality defects, and aggregate-table requirements.
• Coordinate with platform and support teams for workspace, deployment, access, capacity, and service incidents.
• Maintain semantic model diagrams, data dictionaries, measure definitions, source-to-report mappings, dependency information, and report design documentation.
• Provide knowledge transfer and practical support to report developers and analysts using the centralized model.

Required Qualifications

• 3+ years of hands‑on experience developing and supporting Power BI solutions in a production environment.
• Experience designing, maintaining, and troubleshooting shared or centralized Power BI semantic models supporting multiple reports.
• Hands‑on experience with Microsoft Fabric and Direct Lake semantic models.
• Advanced DAX skills, including filter context, relationship behaviour, virtual tables, time intelligence, reusable measures, and performance‑aware calculation design.
• Strong SQL skills for data profiling, reconciliation, root cause analysis, and validation.
• Experience working with Fabric Lakehouses and Delta tables, including validating data between Lakehouse tables and semantic model outputs.
• Experience resolving production incidents involving missing data, unexpected totals, filter behaviour, model changes, or report performance.
• Experience assessing model dependencies and regression impacts before deploying changes.
• Ability to document technical findings clearly and work independently with business and technical teams.

Preferred Qualifications

• Experience with Microsoft Fabric notebooks, PySpark, or Spark SQL for data investigation and light transformation work.
• Experience with deployment pipelines, workspace management, Power BI Apps, Git integration, or Azure DevOps.
• Experience with semantic model metadata and diagnostic tools such as DAX Studio, Tabular Editor, Performance Analyzer, XMLA endpoints, or Semantic Link / SemPy.
• Experience designing aggregate or summary tables for large‑volume reporting workloads.
• Experience troubleshooting Fabric capacity, Direct Lake performance, and concurrency‑related issues.
• Experience working with API‑sourced, event‑based, or contact‑center data.
• Knowledge of medallion architecture and the responsibilities of Bronze, Silver, and Gold data layers.
• Experience with Genesys Cloud data or contact‑center measures such as offered, answered, handled, AHT, service level, CSAT, FCR, agent status, and schedule adherence.

Ideal Candidate Profile

This role is best suited for a hands‑on Analytics Engineer who prefers working with data structures, calculations, relationships, and semantic models rather than focusing primarily on visualization design. The successful candidate should be comfortable moving between Lakehouse data, Direct Lake model behaviour, DAX, and downstream Power BI reports to isolate issues and implement practical fixes.

The candidate should be able to sustain an existing centralized model while improving it safely over time. They should understand that a change to one shared measure, relationship, or table can affect many reports and should therefore approach development with strong dependency analysis, testing, documentation, and change‑control discipline.

This role operates under a hybrid work model and requires employees to work from the Toronto office a minimum of two days per week.