Developer II – Analytics & AI | Montréal (Hybrid Onsite) | Salary Insights + Enterprise Data Engineering

📍 Location: ca

🏷 Type: Contract

Job Intelligence

📍 More in this location:
Browse AI jobs in ca

🏷 Similar roles:
View similar Contract jobs

🌐 Explore all jobs:
View all AI job listings

Job Overview

This role sits within Hydro-Québec’s Analytics and AI ecosystem, focusing on building scalable data-driven solutions that support energy transition initiatives across Quebec. The ideal candidate is a senior-level data/AI engineer with strong experience in cloud analytics platforms, data engineering, and production-grade machine learning workflows. You will translate complex business requirements into robust analytical systems using modern Azure tooling, ensuring reliability, performance, and governance. The position is best suited for professionals who can operate across architecture, development, and stakeholder engagement in large-scale enterprise environments. It requires advanced collaboration and continuous optimization of data pipelines at scale

📅 Job Timeline & Status

This is a mid-to-late cycle active recruitment process with a clearly defined closing deadline. Because the role is still open but approaching its cutoff, candidates should treat this as a high-priority application window. The presence of a fixed deadline indicates structured hiring, likely with multiple candidate reviews already in progress. Applicants should act quickly due to increasing competition as the deadline approaches.

🌍 Work Eligibility & Location

This role is strongly anchored in Québec operations and is expected to require in-person collaboration in Montréal with hybrid flexibility. International applicants may be considered depending on profile strength, but no formal visa sponsorship details are confirmed. Strong French language capability is a significant operational requirement.

💰 Salary Intelligence

This compensation range is competitive within Canadian public-sector energy organizations, especially considering benefits stability and long-term career security. While base salary is strong, the real value comes from benefits, pension structure, and long-term institutional growth opportunities.

📊 Role Breakdown

This position operates at the intersection of enterprise data engineering and applied AI within a large-scale energy utility. You will design and maintain advanced analytics pipelines using Azure Databricks, Azure Data Factory, and Python-based frameworks. Approximately 40% of your work focuses on data ingestion and transformation using ETL and medallion architecture principles, ensuring structured flow from raw to curated datasets. Another 30% involves developing scalable analytics models and integrating machine learning workflows into production systems using MLOps principles. Around 20% of your time is dedicated to stakeholder collaboration, requirements gathering, and solution design across business units. The remaining 10% focuses on testing, documentation, and continuous optimization of distributed data systems leveraging Spark and big data environments. This role demands deep fluency in SQL, Python, and cloud-native architecture patterns, especially within Azure ecosystems.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a Senior-level technical role with a significant barrier to entry due to its requirement for both enterprise-scale data engineering and applied AI knowledge. Candidates are expected to demonstrate strong architectural thinking, not just coding ability. Competition is elevated due to the combination of stable public-sector employment and high-demand AI skill requirements.

🚀 Career Impact

This role provides strong long-term career leverage in enterprise AI and large-scale data infrastructure. Exposure to real-world energy systems and mission-critical analytics significantly strengthens your profile for future roles in AI architecture, platform engineering, and leadership positions in regulated industries.

📋 Key Responsibilities

You will be responsible for designing and implementing end-to-end data and AI solutions using Azure Databricks, Azure Data Factory, and Python. Key duties include building scalable ETL pipelines, optimizing distributed data processing with Spark, and ensuring data quality across enterprise systems. You will collaborate with business stakeholders to translate requirements into technical architectures and deliver production-grade analytics solutions. Additional responsibilities include maintaining CI/CD pipelines, improving model deployment workflows through MLOps practices, and ensuring system reliability. You will also support testing frameworks, documentation standards, and continuous improvement initiatives across analytics platforms.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

Hiring momentum is strong due to the fixed closing deadline, with increasing applicant volume expected as 29/06/2026 approaches. This creates rising competition and faster screening cycles. Candidates applying closer to the deadline may face reduced visibility as shortlisting likely begins before final closure. Expect structured interview phases and multi-stage technical evaluation. Because this is an enterprise AI/data role, early application significantly improves response probability and interview speed expectations.

🔗 Apply for this Job

Apply on Company Site

✅ Job Source & Verification

This listing is sourced from Hydro-Québec’s official careers platform, a high-credibility public-sector employer in Canada’s energy industry. Data reflects the most recent posting cycle with verification as of 16/06/2026. Salary, requirements, and deadlines are aligned with the official job announcement, ensuring high reliability for applicants preparing submissions.