Cloud Data Architect

Procom; Ottawa; On-site; CAD 110,000 - 170,000; Full time

Job summary

Procom is seeking a Cloud Data Architect in Ottawa, Canada to work directly with clients, designing practical cloud-based data architectures using Google Cloud and similar private clouds. You will combine hands-on data engineering, analytics modeling, and client problem solving to ensure data is usable and insights are trusted for campaign performance and growth decisions.

The successful candidate acts as a trusted technical advisor, guiding solution design decisions, integrations, and long-term

Qualifications

• Experience building ETL/ELT pipelines in cloud environments.
• Sophisticated data modeling and data warehousing concepts.
• Proficiency in Google Cloud services and BigQuery.
• Ability to design data solutions for marketing analytics and customer data.

Responsibilities

• Lead technical discovery sessions with clients to define cloud data architectures.
• Integrate data from marketing platforms and CRM to deliver insights.
• Collaborate with clients to understand objectives and success criteria.
• Serve as trusted advisor guiding design decisions and implementations.
• Structure ambiguous requirements and align solutions to business needs.
• Bridge client business and technical teams through delivery.
• Develop data integration for private cloud environments using tools and APIs.
• Build or consult on data extraction, transformation, and loading pipelines.
• Assess pipelines for security, scalability, and data quality.
• Mentor team members and promote best practices.
• Develop reusable data models, pipelines, and dashboard templates.

Skills

ETL/ELT pipelines

Google Cloud

BigQuery

Python

SQL

API integration

OAuth

QA/DevOps

Data modeling

Mentoring

Communication

Client advisory

Education

Computer Science degree

Statistics or Information Systems

Tools

Snowflake

Databricks

dbt

Job description

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
• Strong 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