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Added 9 mins ago
TB
Senior DevOps Engineer (SRE / Platform Engineering)
TLNT Bridge Recruiting
Vancouver, BC
- Full-time
…platform capabilities and are looking for a talented Senior DevOps Engineer with a strong Site Reliability Engineering (SRE) mindset to help take these systems to the next…
Added 9 mins ago
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Senior Associate SIEM Implementation
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- Full-time
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- Full-time
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- $95 to $100 CAD / hour
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- Full-time
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Added 9 mins ago
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Senior Oracle Financials
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- $50 to $55 CAD / hour
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Added 9 mins ago
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DevOps Engineer
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- $65 to $77 CAD / hour
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Added 9 mins ago
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Sr QA Engineer (POS)
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Added 24 mins ago
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Added 24 mins ago
TB
Senior DevOps Engineer (SRE / Platform Engineering)
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Vancouver, BC
- $113,900 to $147,000 CAD / year
- Full-time
…platform capabilities and are looking for a talented Senior DevOps Engineer with a strong Site Reliability Engineering (SRE) mindset to help take these systems to the next…
Added 24 mins ago
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Added 39 mins ago
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Added 39 mins ago
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Hydro One

BI Analytics Engineer

Toronto, ON, Canada

$80 to $90 CAD / hour · Contract

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Job details

Pay: $80 to $90 CAD / hour
Job type: Contract
Apply by: Dec 4, 2026
Experience: 3+ years of hands-on experience developing and supporting Power BI solutions in a production environment.
Posting language: English
Posted: Nov 4, 2026

Original job description

Position Summary

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

Centralized Semantic Model Development & Sustainment

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.

Analytics Engineering, Data Modelling & Validation

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.

Collaboration & Documentation

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.
Strong dimensional modelling skills, including fact and dimension design, star schemas, grain definition, surrogate or business keys, and relationship design.
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-centre data.
Knowledge of medallion architecture and the responsibilities of Bronze, Silver, and Gold data layers.
Experience with Genesys Cloud data or contact-centre 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.

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More about this job

Responsibilities

Maintain and enhance centralized Power BI semantic models and reports, including reusable DAX measures, data relationships, metadata, and Direct Lake integrations. Investigate reporting incidents, validate and reconcile data, optimize performance, and deliver tested, documented changes in collaboration with business, data engineering, and support teams.

Requirements

Requires at least three years of production Power BI development and support experience, including shared semantic models, Microsoft Fabric Direct Lake, dimensional modeling, advanced DAX, and SQL. Candidates must be able to investigate data and reporting issues, assess dependencies and regression risks, validate changes, and communicate findings clearly.

Skills

Power BI
Microsoft Fabric
Direct Lake
Semantic Modeling
Dimensional Modeling
DAX
SQL
Data Validation
Data Reconciliation
Root Cause Analysis
Performance Optimization
Power BI Reporting
Delta Lake
Data Profiling
Incident Resolution
Dependency Analysis

Visa sponsorship

Not detected in the job text

Keywords

Power BI
Microsoft Fabric
Direct Lake
Semantic Models
DAX
SQL
Fabric Lakehouse
Delta Tables
Dimensional Modeling
Star Schema
Fact Tables
Dimension Tables
Data Validation
Data Reconciliation
Root Cause Analysis
Power BI Deployment Pipelines
Power BI Apps
DAX Studio
Tabular Editor
Performance Analyzer
XMLA Endpoints
Semantic Link
PySpark
Spark SQL
Medallion Architecture
Gold Data Layer
Azure DevOps
Git Integration
Contact Center Analytics
Genesys Cloud

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