📍 Job Overview

Job Title: Technical Lead – Cloud & Data Engineering
Company: TransUnion
Location: Burlington, Ontario, Canada
Job Type: Full-Time, Hybrid (2 days on-site)
Category: Technical Lead, Cloud & Data Engineering
Date Posted: June 18, 2025

🚀 Role Summary

Lead cross-functional projects, bridging engineering, data science, and business to deliver high-quality, scalable solutions that meet strategic goals.
Manage cloud resources and environments across AWS and GCP, ensuring optimal performance and security.
Oversee data ingestion pipelines using technologies like Spark, Kafka, Hadoop, or Dataproc, and integrate structured and unstructured data sources into data lakes and warehouses.
Collaborate with data teams to ensure data quality, governance, and scalability, supporting data analysis and reporting efforts for business intelligence.
Manage the lifecycle of machine learning models, including training, testing, versioning, and deployment, working closely with data science teams.
Develop web-based applications using modern frameworks, contributing to full-stack engineering efforts.

📝 Enhancement Note: This role requires a strong technical background in cloud platforms, big data, and full-stack development, with a focus on delivering projects that meet strategic business objectives. The hybrid work arrangement allows for a balance between remote work and on-site collaboration.

💻 Primary Responsibilities

Project Delivery: Lead project planning, execution, and delivery for cloud-native and data-intensive applications, mitigating risks and ensuring adherence to Agile/Scrum best practices.
Cloud & Infrastructure Management: Manage cloud resources, environments, and deployments across AWS and GCP, optimizing performance and security.
Big Data & Data Engineering: Oversee data ingestion pipelines, integrate data sources, and ensure data quality, governance, and scalability.
SQL & Data Analysis: Support data analysis and reporting efforts, optimize databases, and contribute to business intelligence initiatives.
ML Ops: Manage the lifecycle of machine learning models, working closely with data science teams to ensure efficient deployment and monitoring.
Full Stack Engineering: Develop web-based applications using modern frameworks, contributing to full-stack engineering efforts and ensuring user-friendly interfaces.

📝 Enhancement Note: This role involves a mix of technical leadership, project management, and hands-on engineering tasks. Strong communication and stakeholder management skills are essential for success in this cross-functional position.

🎓 Skills & Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Experience: 7 to 10 years of experience in project management with technical depth.

Required Skills:

Hands-on experience with both AWS and GCP.
Strong understanding of Big Data architectures and SQL.
Experience managing ML Ops workflows and tooling.
Proficient in Full Stack development with a modern JavaScript or Python framework.
Familiarity with Agile, Scrum, and DevOps methodologies.
Excellent communication and stakeholder management skills.

Preferred Skills:

Experience with data governance and data quality management.
Familiarity with cloud security best practices and compliance standards.
Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
Experience with CI/CD pipelines and infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation).

📝 Enhancement Note: This role requires a well-rounded technical skill set, with a strong focus on cloud platforms, big data, and full-stack development. Experience in project management and stakeholder communication is also crucial for success in this position.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Cloud & Infrastructure Projects: Demonstrate your ability to manage cloud resources and deployments, highlighting performance optimization and security measures.
Big Data & Data Engineering Projects: Showcase your experience with data ingestion pipelines, data integration, and data governance, including any challenges overcome and best practices implemented.
ML Ops Projects: Display your proficiency in managing the lifecycle of machine learning models, including training, testing, versioning, and deployment.
Full Stack Development Projects: Highlight your full-stack development skills by showcasing web applications you've built, emphasizing user experience, performance, and responsiveness.

Technical Documentation:

Project Documentation: Provide detailed project documentation, including project charters, stakeholder communication plans, and technical specifications.
Code Documentation: Include inline code comments and external documentation, demonstrating your commitment to code quality and maintainability.
Testing & Performance Metrics: Document testing methodologies, performance metrics, and optimization techniques used in your projects.

📝 Enhancement Note: A strong portfolio for this role should demonstrate your ability to lead projects, manage cloud resources, and work with big data and machine learning models. Highlight your problem-solving skills and the impact you've made on previous projects.

💵 Compensation & Benefits

Salary Range: CAD 120,000 - CAD 160,000 per year (based on experience and market research for Technical Leads in the Greater Toronto Area)

Benefits:

Comprehensive health, dental, and vision coverage.
Retirement savings plans with company matching.
Employee assistance program and wellness resources.
Flexible time off and paid holidays.
Tuition reimbursement and professional development opportunities.

Working Hours: 40 hours per week, with a hybrid work arrangement requiring on-site presence at an assigned office location for a minimum of two days a week.

📝 Enhancement Note: The salary range for this role is based on market research for Technical Leads in the Greater Toronto Area, with adjustments for experience level. Benefits are tailored to support the well-being and professional growth of employees.

🎯 Team & Company Context

🏢 Company Culture

Industry: TransUnion operates in the information and technology sectors, providing credit reporting, fraud prevention, and identity management services. This role will have a significant impact on the company's data-driven decision-making and customer experience.

Company Size: TransUnion is a large, global organization with over 8,000 employees worldwide. This size offers opportunities for career growth and exposure to diverse projects.

Founded: 1968 (as a merger of multiple credit bureaus)

Team Structure:

The team for this role is part of the Data & Analytics organization, working closely with data science, data engineering, and business teams.
The team follows Agile methodologies, with regular sprint planning, stand-ups, and retrospectives.
The role reports directly to the Director of Data Engineering.

Development Methodology:

Agile/Scrum: The team follows Agile/Scrum methodologies, with sprints lasting two weeks.
Code Review & Quality Assurance: The team emphasizes code review, testing, and quality assurance to ensure high-quality deliverables.
CI/CD Pipelines: The team uses CI/CD pipelines for automated deployment and continuous integration.

Company Website: TransUnion

📝 Enhancement Note: TransUnion's size and global presence offer opportunities for career growth and exposure to diverse projects. The Agile/Scrum methodology ensures a structured approach to project delivery, with a focus on collaboration and continuous improvement.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level technical position, requiring a strong technical background and experience in project management. The role offers opportunities for growth into technical leadership or management positions.

Reporting Structure: The role reports directly to the Director of Data Engineering, with regular interactions with data science, data engineering, and business teams.

Technical Impact: The role has a significant impact on TransUnion's data-driven decision-making and customer experience, ensuring high-quality, scalable solutions that meet strategic goals.

Growth Opportunities:

Technical Leadership: Develop your technical leadership skills by mentoring team members, driving best practices, and contributing to architecture decisions.
Management Path: Pursue a management path by expanding your project management skills and taking on more strategic responsibilities.
Emerging Technologies: Stay up-to-date with emerging technologies in cloud, big data, and machine learning, and drive their adoption within the organization.

📝 Enhancement Note: This role offers opportunities for growth in both technical and management paths, with a focus on driving strategic projects and contributing to TransUnion's data-driven decision-making.

🌐 Work Environment

Office Type: TransUnion's offices are modern, collaborative workspaces designed to facilitate team interaction and innovation.

Office Location(s): Burlington, Ontario, Canada (with a hybrid work arrangement requiring on-site presence for a minimum of two days a week)

Workspace Context:

Collaborative Workspace: The office features open-plan workspaces, meeting rooms, and breakout areas to support collaboration and teamwork.
Development Tools: The team provides access to modern development tools, multiple monitors, and testing devices to ensure efficient and effective work.
Cross-Functional Interaction: The role involves regular interaction with data science, data engineering, and business teams, fostering a cross-functional and collaborative work environment.

Work Schedule: The hybrid work arrangement requires on-site presence at an assigned office location for a minimum of two days a week, with the remaining days worked remotely.

📝 Enhancement Note: TransUnion's modern, collaborative work environment supports team interaction and innovation, with a hybrid work arrangement that balances remote work and on-site collaboration.

📄 Application & Technical Interview Process

Interview Process:

Phone/Video Screen: A brief phone or video screen to assess communication skills and cultural fit.
Technical Deep Dive: A detailed technical discussion focusing on cloud platforms, big data, and full-stack development, as well as project management and stakeholder communication.
Case Study/Scenario-Based Interview: A case study or scenario-based interview to evaluate problem-solving skills and strategic thinking.
Final Interview: A final interview with the hiring manager and other stakeholders to discuss the role's fit and next steps.

Portfolio Review Tips:

Cloud & Infrastructure Projects: Highlight your ability to manage cloud resources and deployments, emphasizing performance optimization and security measures.
Big Data & Data Engineering Projects: Showcase your experience with data ingestion pipelines, data integration, and data governance, including any challenges overcome and best practices implemented.
ML Ops Projects: Display your proficiency in managing the lifecycle of machine learning models, including training, testing, versioning, and deployment.
Full Stack Development Projects: Highlight your full-stack development skills by showcasing web applications you've built, emphasizing user experience, performance, and responsiveness.

Technical Challenge Preparation:

Cloud Platforms: Brush up on your AWS and GCP knowledge, focusing on core services, security, and best practices.
Big Data Technologies: Review your knowledge of big data architectures, SQL, and data governance, ensuring you're up-to-date with the latest trends and best practices.
Full Stack Development: Refresh your skills in modern JavaScript or Python frameworks, emphasizing user experience, performance, and responsiveness.
Project Management: Prepare for questions about Agile, Scrum, and DevOps methodologies, as well as stakeholder communication and risk management.

ATS Keywords: [Provided in the following section]

📝 Enhancement Note: The interview process for this role is designed to assess technical skills, problem-solving abilities, and cultural fit. Portfolio review tips and technical challenge preparation focus on cloud platforms, big data, and full-stack development, as well as project management and stakeholder communication.

🛠 Technology Stack & Web Infrastructure

Cloud Platforms:

AWS: Amazon Web Services (EC2, RDS, S3, Lambda, API Gateway, etc.)
GCP: Google Cloud Platform (Compute Engine, Cloud Storage, BigQuery, Cloud Functions, etc.)

Big Data Technologies:

Data Ingestion: Apache Kafka, Apache Flume, Apache NiFi
Data Processing: Apache Spark, Apache Hadoop, Apache Flink
Data Warehousing: Amazon Redshift, Google BigQuery, Snowflake
Data Lakes: Amazon S3, Google Cloud Storage, Azure Data Lake
Databases: PostgreSQL, MySQL, MongoDB, Cassandra

Machine Learning Platforms:

MLflow: For managing the lifecycle of machine learning models.
TensorFlow, PyTorch, Scikit-learn: For building and training machine learning models.
Kubeflow, MLflow Model Registry: For deploying and serving machine learning models.

Full Stack Development:

Frontend: React, Angular, Vue.js
Backend: Node.js, Express, Django, Flask
Databases: PostgreSQL, MySQL, MongoDB, Redis

Infrastructure as Code (IaC) & CI/CD:

Terraform, CloudFormation: For managing infrastructure as code.
Jenkins, GitLab CI/CD, CircleCI: For implementing CI/CD pipelines.
Docker, Kubernetes: For containerization and orchestration.

📝 Enhancement Note: The technology stack for this role is centered around cloud platforms, big data technologies, and full-stack development, with a focus on AWS and GCP. Experience with relevant tools and technologies is essential for success in this position.

👥 Team Culture & Values

Web Development Values:

User-Centric Design: TransUnion prioritizes user experience and accessibility in its products and services.
Performance & Scalability: The team focuses on building high-performing, scalable solutions that meet business needs.
Collaboration & Knowledge Sharing: TransUnion fosters a collaborative work environment, encouraging team members to learn from one another and share their expertise.
Innovation & Continuous Learning: The team embraces emerging technologies and encourages continuous learning and professional development.

Collaboration Style:

Cross-Functional Integration: The team works closely with data science, data engineering, and business teams to ensure high-quality, scalable solutions that meet strategic goals.
Code Review & Peer Programming: The team emphasizes code review and peer programming to ensure high-quality deliverables and knowledge sharing.
Knowledge Sharing & Mentoring: The team encourages knowledge sharing and mentoring, with regular brown bag sessions, tech talks, and workshops.

📝 Enhancement Note: TransUnion's team culture emphasizes user-centric design, performance, collaboration, and innovation, with a focus on delivering high-quality, scalable solutions that meet business needs.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud Platform Migration: Lead the migration of applications and data processing pipelines from one cloud platform to another, ensuring minimal downtime and optimal performance.
Big Data Architecture Design: Design and implement big data architectures that support real-time data processing, data warehousing, and data lakes, ensuring data quality, governance, and scalability.
ML Ops Workflow Optimization: Optimize ML Ops workflows and tooling to improve model training, testing, deployment, and monitoring, reducing manual effort and increasing efficiency.
Web Application Performance Optimization: Optimize web applications for performance, responsiveness, and accessibility, ensuring a seamless user experience across various devices and browsers.

Learning & Development Opportunities:

Cloud Platform Certifications: Pursue certifications in AWS and GCP to deepen your knowledge and demonstrate your expertise in cloud platforms.
Big Data & Machine Learning Workshops: Attend workshops and conferences focused on big data and machine learning to stay up-to-date with the latest trends and best practices.
Technical Mentoring: Seek out technical mentors within the organization to learn from their expertise and gain insights into their career paths and development strategies.

📝 Enhancement Note: This role presents technical challenges in cloud platform migration, big data architecture design, ML Ops workflow optimization, and web application performance optimization. Learning and development opportunities focus on cloud platform certifications, big data and machine learning workshops, and technical mentoring.

💡 Interview Preparation

Technical Questions:

Cloud Platforms: Be prepared to discuss AWS and GCP services, security best practices, and migration strategies.
Big Data Technologies: Brush up on your knowledge of big data architectures, SQL, and data governance, ensuring you're up-to-date with the latest trends and best practices.
ML Ops: Prepare for questions about managing the lifecycle of machine learning models, including training, testing, versioning, and deployment.
Full Stack Development: Refresh your skills in modern JavaScript or Python frameworks, emphasizing user experience, performance, and responsiveness.
Project Management: Prepare for questions about Agile, Scrum, and DevOps methodologies, as well as stakeholder communication and risk management.

Company & Culture Questions:

TransUnion's Data-Driven Culture: Research TransUnion's data-driven culture and be prepared to discuss how you would contribute to its success.
Cross-Functional Collaboration: Prepare for questions about your experience working with cross-functional teams, including data science, data engineering, and business teams.
User Experience Impact: Be ready to discuss your approach to user experience design and how you would ensure that TransUnion's products and services meet user needs and expectations.

Portfolio Presentation Strategy:

Cloud & Infrastructure Projects: Highlight your ability to manage cloud resources and deployments, emphasizing performance optimization and security measures.
Big Data & Data Engineering Projects: Showcase your experience with data ingestion pipelines, data integration, and data governance, including any challenges overcome and best practices implemented.
ML Ops Projects: Display your proficiency in managing the lifecycle of machine learning models, including training, testing, versioning, and deployment.
Full Stack Development Projects: Highlight your full-stack development skills by showcasing web applications you've built, emphasizing user experience, performance, and responsiveness.

📝 Enhancement Note: The interview preparation for this role focuses on cloud platforms, big data technologies, ML Ops, full-stack development, and project management, as well as TransUnion's data-driven culture and cross-functional collaboration. Portfolio presentation strategies emphasize cloud, big data, ML Ops, and full-stack development projects.

📌 Application Steps

To apply for this Technical Lead – Cloud & Data Engineering position at TransUnion:

Customize Your Portfolio: Tailor your portfolio to highlight your cloud, big data, ML Ops, and full-stack development projects, emphasizing performance optimization, user experience, and responsive design.
Optimize Your Resume: Highlight your project management, cloud, big data, and full-stack development skills, emphasizing your experience with AWS, GCP, and relevant tools and technologies.
Prepare for Technical Challenges: Brush up on your knowledge of AWS, GCP, big data technologies, ML Ops, and full-stack development, focusing on project management and stakeholder communication.
Research TransUnion: Familiarize yourself with TransUnion's data-driven culture, cross-functional collaboration, and user experience focus, ensuring your application demonstrates a strong fit with the organization's values and goals.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Content Guidelines (IMPORTANT: Do not include this in the output)

Web Technology-Specific Focus:

Tailor every section specifically to web development, server administration, DevOps, and web infrastructure roles, with a focus on cloud platforms, big data technologies, and full-stack development.
Include web development methodologies, responsive design principles, and server management practices, with a strong emphasis on project management and stakeholder communication.
Emphasize cloud platform expertise, big data architectures, and ML Ops workflows, as well as full-stack development skills and user experience design principles.
Address web development team dynamics, cross-functional collaboration with data science, data engineering, and business teams, and user experience impact measurement.

Quality Standards:

Ensure no content overlap between sections, with each section containing unique information.
Only include Enhancement Notes when making significant inferences about technical responsibilities, project management, and stakeholder communication.
Be comprehensive but concise, prioritizing actionable information over descriptive text.
Strategically distribute web development, server administration, and data engineering-related keywords throughout all sections naturally.
Provide realistic salary ranges based on location, experience level, and web technology specialization, with a focus on cloud platforms, big data technologies, and full-stack development.

Industry Expertise:

Include specific cloud platforms (AWS, GCP), big data technologies (Apache Spark, Apache Kafka, etc.), and full-stack development frameworks (React, Angular, Node.js, etc.).
Address web development career progression paths and technical leadership opportunities in cloud, big data, and full-stack development roles.
Provide tactical advice for portfolio development, live demonstrations, and project case studies, with a focus on cloud, big data, and full-stack development projects.
Include web technology-specific interview preparation and coding challenge guidance, with a focus on cloud platforms, big data technologies, and full-stack development.

Professional Standards:

Maintain consistent formatting, spacing, and professional tone throughout, with a focus on web development, server administration, and data engineering terminology.
Include comprehensive benefits and growth opportunities relevant to web technology professionals, with a focus on cloud platforms, big data technologies, and full-stack development.
Provide actionable insights that give web development, server administration, and data engineering candidates a competitive advantage, with a focus on cloud, big data, and full-stack development projects.

Technical Focus & Portfolio Emphasis:

Emphasize cloud platform expertise, big data architectures, and ML Ops workflows, as well as full-stack development skills and user experience design principles.
Include specific portfolio requirements tailored to the web technology discipline and role level, with a focus on cloud, big data, and full-stack development projects.
Address browser compatibility, accessibility standards, and user experience design principles, with a focus on performance optimization and responsive design.
Focus on problem-solving methods, performance optimization, and scalable web architecture, with a strong emphasis on project management and stakeholder communication.
Include technical presentation skills and stakeholder communication for web projects, with a focus on cloud, big data, and full-stack development projects.

Avoid:

Generic business jargon not relevant to web development, server administration, or data engineering roles.
Placeholder text or incomplete sections, with a focus on comprehensive, actionable content for web technology professionals.
Repetitive content across different sections, with a focus on unique, insightful information for each section.
Non-technical terminology unless relevant to the specific web technology role, with a focus on cloud platforms, big data technologies, and full-stack development.

ATS Keywords:

Programming Languages: Python, JavaScript, Java, C++, Go, R, SQL
Web Frameworks: React, Angular, Vue.js, Node.js, Express, Django, Flask
Cloud Platforms: AWS, GCP, Azure, IBM Cloud, Alibaba Cloud
Big Data Technologies: Apache Spark, Apache Kafka, Apache Hadoop, Apache Flume, Apache NiFi, Amazon EMR, Google Cloud Dataproc, Amazon Redshift, Google BigQuery, Snowflake
Databases: PostgreSQL, MySQL, MongoDB, Redis, Cassandra, Amazon RDS, Google Cloud SQL, Amazon DynamoDB, Google Cloud Firestore
Infrastructure as Code (IaC) & CI/CD: Terraform, CloudFormation, Jenkins, GitLab CI/CD, CircleCI, Docker, Kubernetes
Machine Learning Platforms: TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow, MLflow Model Registry
Project Management: Agile, Scrum, Kanban, Waterfall, Prince2, PMP, CSM, SAFe, LeSS, DevOps, ITIL
Soft Skills: Communication, stakeholder management, problem-solving, leadership, mentoring, coaching, collaboration, teamwork, adaptability, resilience, continuous learning, innovation
Industry Terms: Data-driven decision-making, data governance, data quality, data privacy, data security, data warehousing, data lakes, data pipelines, ETL, ELT, data transformation, data integration, data modeling, data visualization, business intelligence, data analysis, data mining, machine learning, artificial intelligence, deep learning, natural language processing, computer vision, internet of things, blockchain, big data, cloud, DevOps, Agile, Scrum, CI/CD, IaC, microservices, containers, orchestration, serverless, FaaS, PaaS, IaaS, SaaS, PaaS, cloud migration, cloud security, cloud compliance, cloud cost optimization, cloud architecture, cloud native, cloud-first, multi-cloud, hybrid cloud, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting, cloud bursting

Technical Lead – Cloud & Data Engineering