Data Architect | Canada

Cuesta Partners, LLC

United States

Hybrid

USD 120,000 - 150,000

Full time

14 days+

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Benefits offered by this job

Exposure to AI learning

Flexible location

Team collaboration

Bonus potential

Health insurance

Job summary

Cuesta Partners, LLC seeks a Data Architect to lead transformational data programs and elevate AI and data capabilities across client engagements. The role focuses on business data modeling, modern data delivery patterns, and master data governance, with an emphasis on designing scalable data ecosystems and acting as a bridge between executives and technical teams.

You’ll guide model reviews, define data architectures, and lead teams of data engineers and ML professionals to deliver data

Qualifications

Bachelor’s degree in a technical or quantitative field.
10+ years of work experience in the data analytics space.
Hands-on experience using AI-assisted development tools in daily workflow.
Previous consulting experience is a plus.

Responsibilities

Design the business data model based on discovered processes and data analysis.
Translate business requirements into technical design specifications (data streams, integrations, transformations, databases, warehouses).
Develop work estimates for Data Warehouse & Data Lake deliverables.
Coach and mentor a team of data engineers, analysts, and ML engineers on data architecture and modeling best practices.

Skills

Data modeling

Data governance

AI tooling

Cloud platforms

Education

Bachelor’s degree in a technical or quantitative field

Tools

Cursor

Claude Code

Codex

Job description

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.

Key Areas of Focus:

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
Data acquisition & integration
Data transformation & orchestration
Vis:

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

What You’ll Do

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

What We’re Looking For

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.
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
Hands-on experience deploying solutions in large-scale, high performing databases
Expertise aligned to technologies listed in Key Areas of Focus

Benefits: What you’ll gain

At Cuesta, we value entrepreneurship, humility, diversity, learning, speed, and balance. We provide our team members with: