One of our clients is looking for a Cloud Data Architect works directly with clients to understand their challenges and designs practical cloud based solutions in Google Cloud and similar private cloud environments. Combining hands on data engineering, analytics modeling, and client problem solving, this role ensures data is usable, insights are trusted, and marketing and business teams, such as paid media, CRM/lifecycle, and growth teams, can make informed decisions with confidence around campaign performance, customer acquisition, and retention.
The successful candidate will act as a trusted technical advisor, helping clients navigate complex data challenges while delivering practical, production ready solutions.
Primary Responsibilities Lead technical discovery sessions with clients to understand business challenges, define solution approaches, and recommend cloud data architectures that meet functional and strategic objectives
Integrate data from marketing platforms (e.g., paid media, analytics, CRM) and apply domain knowledge to frame business problems and deliver insights aligned with marketing performance objectives
Collaborate directly with clients to understand business objectives, technical requirements, and success criteria
Serve as a trusted technical advisor and partner to clients, guiding them through solution design decisions, implementation approaches, trade offs and best practices and supporting long term success
Navigate ambiguous and evolving requirements by structuring problems, validating assumptions, and aligning technical solutions to real business needs without relying on incomplete or incorrect inputs
Act as the conduit between client business,



marketing and their technical teams to ensure alignment from technical problem definition through delivery
Develop data integration for customer private cloud environments, leveraging available tools in that platform, APIs, and scripting
Build, or consult on, infrastructure required for optimal extraction, transformation, and loading of data across diverse data sources
Assess existing customer pipelines and offer improvements for efficiency, security, scalability and data quality
Mentor and support team members through technical guidance and knowledge sharing to help elevate team capabilities and promote best practices
Develop reusable data models, pipelines, and dashboard templates that improve delivery consistency and accelerate future client engagements
Understand cloud infrastructure and operational requirements sufficiently to anticipate how architectural decisions impact reliability, performance, and business outcomes
Skills and Experience Required Proficiency building ETL/ELT pipelines in private GCP environments
Experience with Google Cloud, especially BigQuery
Experience designing and implementing data solutions for marketing, advertising, or customer analytics use cases, including integration with Google Marketing Platform products and related marketing vendor APIs
Strong Computer Science (CS) fundamentals,



problem solving skills and software engineering skills
A strong ability to understand and organize data from various sources
Strong expertise in a programming language (preferably Python)
Proficiency writing queries with SQL
Experience building solutions via API integration
Knowledge of OAuth protocols for API authentication
Experience with quality assurance (QA) and devops processes
Strong understanding of security and privacy implications in data pipelines
Ability to identify and resolve performance and data quality issues in data pipelines
Solid critical thinking and problem solving skills with attention to detail
Experience mentoring technical colleagues or leading technical discussions
Ability to influence technical decision making, facilitate solution discussions, and build consensus with client and internal stakeholders
Excellent written, verbal, and presentation skills, with the ability to communicate complex technical concepts to both technical and non-technical audiences
Ability to prioritize projects and handle multiple tasks efficiently
A degree in Computer Science, Statistics, Information Systems, or other quantitative fields, or comparable industry experience
Preferred Experience Experience with a range of data warehousing and integration platforms and software, such as Snowflake, Databricks and dbt
Experience with a wide variety of APIs for marketing platforms and products
Experience with AI deployment in cloud environments, especially Gemini
Google Cloud Professional certifications, particularly the Data Engineer, Cloud Database Engineer or ML Engineer certifications, are an asset

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📌 Cloud Data Architect (Quebec City)
🏢 Procom
📍 Quebec City