Data Architect | Canada

Cuesta Partners

1d•CA$180,000 - CA$225,000•Remote

About The Position

Cuesta Partners is looking for a Data Architect to engage with us on transformational data programs with companies looking to take their AI & data capabilities to the next level.

Requirements

Business data modeling
Trade-offs between different modeling philosophies – dimensional, 3NF, Data Vault
Conceptual vs physical modeling
Modeling techniques such as inheritance, parent / child tables, ragged structures, slowly changing dimensions etc.
Normalization vs de-normalization trade-offs
Detailed understanding the design trade-offs around different modeling approaches
Ability to lead model review sessions, and being able to lay out the design "options" and implications, and also present it in a way that both executives and technical-minded people can understand
Modern data delivery design patterns
Data as product
Design compromises & considerations
Know what exemplar deliverables look like
Team composition and responsibilities / work to be done
Streaming vs batch design patterns / considerations
Pros / cons of data mesh delivery model vs alternatives
Comparison of modern cloud native platforms vs legacy on-premises data solutions
Master data governance
Types and most common root cause of DQ issues
Remediation approaches
MDM architecture styles / patterns
Key capabilities of MDM & DQ vendors
Expert in technologies including 1 or more of each class:
Data management layer: Snowflake, Databricks, Microsoft Fabric, GCP Big Query/ AWS DB Options
Data acquisition & integration: Azure Data Factory (ADF), Matillion, FiveTran & HVR, Keboola
Data transformation & orchestration: ETL, DBT, Python / SQL, Apache Airflow etc.
Vis: PowerBI, Tableau, Looker, Domo / ThoughtSpot / Qlik / platform BI vendors (ORCL, SAP, AWS etc.)
Architecture transformations: Considerations / experience evaluating lift & shift vs re-model trade-offs, Considerations / experience when consolidating decentralized silos, Considerations / experience when moving to modern cloud stacks, Considerations / experience when enabling unified operational & analytics data hubs
Communication & leadership skills: Able to identify & evaluate most important criteria when making design decisions, Able to look around corners – recognize likely issues before they happen, Able to communicate complex subjects with executive leaders, Able to solicit input & feedback to model and design decisions, Ability to teach / leverage experience to develop team/talent around them, Use past experiences to help with change management to eases concerns over shifts in approach
Bachelor’s degree in a technical or quantitative field (e.g. Computer Science, Math, Economics Statistics)
10+ years of work experience in the data analytics space
Active, hands-on experience using AI-assisted development tools (e.g., Cursor, Claude Code, Codex, or similar) in your daily workflow — including building working prototypes, agentic pipelines, RAG systems, or LLM-powered data and analytics products — is a strong asset.
Hands-on experience deploying solutions in large-scale, high performing databases
Expertise aligned to technologies listed in Key Areas of Focus

Nice To Haves

Previous experience in the consulting space is a plus
A passion for exploring and solving different kinds of problems
A desire to learn and assimilate technical information quickly

Responsibilities

Design the business data model based on the discovered business processes and data analysis
Set the standard for AI-augmented development practices — ensuring projects are structured and tooled in ways that allow coding agents to operate effectively, and coaching the team to work the same way.
Translate business requirements into technical design specifications, including data streams, integrations, transformations, databases, and data warehouses.
Develop work estimates for Data Warehouse & Data Lake deliverables
Coach and mentor a team of a few dozen data engineers, analysts and ML Engineers on data architecture and modeling best practices
Define the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client categories, and master data such as clients, vendors, materials, and employees
Define reference architecture, which is a pattern others can follow to create and improve data systems
Define data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition
Collaborate and coordinate with team members, clients and external SMEs