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Data Model Jobs in Edmonton, AB (NOW HIRING)

Work with development teams to ensure future data model changes in operational systems are incorporated into the warehouse. Ensure standards and best practices are incorporated into data solutions.

Developing enterprise grade Power BI / Analysis Services semantic models. Optimizing existing ETL process and data transformations. Create technical documentation for solutions that have been built.

Data Management

Edmonton, AB ยท On-site

CA$1 - CA$2/hr

Data Management Overview In this role, you will be responsible for managing and analyzing capital ... You will develop and maintain tools, models, and reporting systems that provide visibility into ...

Update existing data models and architecture documentation. * Support data governance and data quality initiatives. * Participate in architecture reviews and design sessions. * Develop transition ...

Monitor model and pipeline performance in production; identify and resolve issues proactively ... Proficiency in Python and at least one data processing framework (Spark, dbt, Airflow, Prefect, or ...

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Data Model information

What is the difference between Data Model vs Data Analyst?

AspectData ModelData Analyst
Primary RoleDesigns and structures data for databases and systemsAnalyzes data to generate insights and reports
Skills & CertificationsDatabase design, data modeling, SQL, certifications like CDMPData analysis, statistics, Excel, SQL, certifications like CAP or Microsoft Data Analyst
Work EnvironmentWorks with database systems, data warehouses, IT teamsWorks with business teams, reports, dashboards, data visualization tools
Industry UsageUsed in database design, data architecture, software developmentUsed in business intelligence, reporting, data-driven decision making

While Data Modelers focus on designing the structure of data systems, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills, but their primary objectives differ: one builds the foundation, the other analyzes the data.

What are the 4 types of data models?

Data models are classified into four main types: conceptual, logical, physical, and view models. Conceptual models define high-level data structures, logical models specify detailed structures without regard to physical storage, physical models describe how data is stored in hardware, and view models focus on specific user perspectives. Data modelers and database administrators often use these types to design and optimize databases effectively.

What is the salary of a data modeler?

The salary of a data modeler typically ranges from $70,000 to $120,000 annually, depending on experience, location, and industry. Skilled data modelers with expertise in database design, data warehousing, and tools like SQL or ER modeling tend to earn higher salaries.

What does a data modeler do?

A data modeler designs and creates data models to organize and structure data for databases and information systems. They analyze data requirements, develop schemas, and use tools like ER diagrams to ensure data consistency and efficiency. Strong knowledge of database systems and data modeling techniques is essential for this role.

Will AI replace data modelers?

AI tools can automate certain tasks in data modeling, such as data cleaning and pattern recognition, but data modelers are essential for designing complex data structures, interpreting business needs, and ensuring data quality. The role is evolving to include working alongside AI and developing expertise in data management and modeling techniques. Continuous learning and proficiency with modeling tools remain important for data modelers to stay relevant.
What cities near Edmonton, AB are hiring for Data Model jobs? Cities near Edmonton, AB with the most Data Model job openings:

Data Analyst (Hybrid) JP922

P@thlion Staffing Careers

Edmonton, AB โ€ข Hybrid

Full-time

Posted 2 days ago

Be an early applicant


Job description

Working alongside existing programs and technical staff, the Data Analyst provides focused analytical capacity during the model design and build phases. This includes validating and testing core data models, canonical definitions, and Unified Business Object designs using real environmental monitoring and regulatory data to ensure the data foundation reflects how the business operates.
Responsibilities:
A. Model Validation
Test proposed canonical elements against actual datasets.
Validate Unified Business Object model assumptions using:
o Historical monitoring data
o Reporting templates
o Program specific datasets
Surface edge cases early during design and build phases.
B. Semantic analysis at the core data layers
Analyze:
o Measurement comparability
o Unit normalization issues
o Data type and temporal consistency (e.g., sample dates vs reporting dates)
Identify where:
o Standardization is safe vs. standardization would break meaning
Produce evidence-based recommendations to support the data architecture.
C. Analytical Prototyping in the Data Management Platform
Build exploratory analytical views to test:
o Aggregation behavior
o Trend stability
o Query feasibility at scale, especially for large, ambient monitoring datasets
Answer questions like:
o Can this model support cross program trend analysis?
o Does the chosen data model work at scale?