About the role
This role bridges engineering, analytics, and business—turning data into insights that drive business and marketing decisions. The Senior 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.
Required Skills & Experience
Proficiency building ETL/ELT pipelines in private cloud environments (Google Cloud, AWS, Azure).
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 with Google Cloud, especially BigQuery.
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 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 Skills & 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 & products.
Experience with AI deployment in cloud environments, especially Gemini.
Experience with Kotlin/JVM or JavaScript in addition to Python is an asset.
Google Cloud Professional certifications, particularly the Data Engineer, Cloud Database Engineer or ML Engineer certifications, are an asset.
Location: Remote - based in Canada
Napkyn welcomes and encourages applications from people with disabilities. Accommodations are available upon request for applicants in all aspects of the selection process. We are committed to diversity of background, thought and experience, and we work to create an environment in which all our employees thrive by bringing their authentic self to work.
Kepler is a people first organization. If this role piques your interest but you may not check every box, we still encourage you to apply! Studies show that imposter syndrome can prevent women and people of color from applying unless they meet every single qualification. We welcome all who are interested to apply, you just might be a great candidate for this role or others.
Protect yourself from recruitment fraud. The only way to apply for a position at Kepler is by submitting a direct application via the Keplergrp.com website or working with a recruiter employed by Kepler with a @keplergrp.com email address. Learn how to stay safe by clicking here
