Cuesta Partners
Consulting
DataArchitect
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Architect at Cuesta Partners. Skills: Data Architecture, Data Modeling, Cloud Data Platforms, Data Governance. Design business data model. Set standard for AI-augmented development”
Industry & Context.
Root cause analysis; Design decision evaluation
What They're Looking For.
Must Have
Bachelor's in technical or quantitative field, 10+ years of work experience, Hands-on experience deploying solutions, SQL proficiency
Nice to Have
AI-assisted development tools experience, Previous experience in consulting, PhD preferred
What You'll Do.
Design business data model
Set standard for AI-augmented development
Translate business requirements
Develop work estimates
Mentor data engineers
Define data architecture framework
Define reference architecture
Collaborate with team members
Coordinate with clients
How You'll Work.
Team & Collaboration
Cross-functional teams; Client collaboration; SME coordination
Communication Scope
Executive presentations; Technical communication
Full 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 - 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 / expe
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