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Data Analysis Manager Jobs in Alberta (NOW HIRING)

Collaborate with data specialist, business analysts, management, and other team members to gather requirements and deliver user-centered solutions. Ensure compliance with institutional and ITS ...

Extract data from MLS System, Dynamics CRM, and external sources. Maintain databases * Use appropriate software to organize and analyze large volumes of data and continuously improve and automate ...

You will manage evolving data needs, support diverse project teams, and ensure high quality ... Experience in environmental field sampling, chemistry, analytical data validation, groundwater ...

The successful candidate will work across data analysis and solution development initiatives to ... Executes change management plans to drive acceptance, adoption, minimize resistance, and maximize ...

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Data Analysis Manager information

See Alberta salary details

$20K

$87K

$146.5K

How much do data analysis manager jobs pay per year?

As of May 29, 2026, the average yearly pay for data analysis manager in Alberta is $87,044.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,000.00 and $110,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Analysis Manager, and why are they important?

To thrive as a Data Analysis Manager, you need advanced analytical skills, a strong background in statistics or data science, and relevant experience often backed by a degree in mathematics, computer science, or a related field. Expertise in tools such as SQL, Python, R, and business intelligence platforms, along with proficiency in data visualization software like Tableau or Power BI, is typically required. Leadership, effective communication, and problem-solving abilities are crucial soft skills for managing teams and translating complex data insights into actionable business strategies. These skills and qualities are essential for driving data-informed decision-making and ensuring the success of analytics initiatives within an organization.

How does a Data Analysis Manager typically collaborate with other departments within an organization?

A Data Analysis Manager regularly partners with teams such as marketing, finance, operations, and IT to identify data needs and translate business questions into actionable analysis. They facilitate communication between data analysts and stakeholders, ensuring that data insights are aligned with organizational goals. By leading cross-functional meetings and presenting findings to non-technical audiences, they help drive data-informed decision-making across the company. This collaborative approach not only enhances the impact of analytics but also fosters a culture of data literacy throughout the organization.

What does a Data Analysis Manager do?

A Data Analysis Manager oversees teams that collect, process, and interpret data to help organizations make informed business decisions. They are responsible for managing data projects, ensuring data quality, and translating complex data findings into actionable insights for stakeholders. Additionally, they often coordinate with other departments, set analytical strategies, and mentor data analysts. Their role is crucial for driving data-driven decision-making in a company.

What is the difference between Data Analysis Manager vs Data Analyst?

AspectData Analysis ManagerData Analyst
ResponsibilitiesOversees data analysis projects, manages teams, develops strategiesPerforms data collection, cleaning, and analysis to support business decisions
Required SkillsLeadership, project management, advanced analyticsStatistical analysis, data visualization, technical skills
QualificationsBachelor's or master's in data science, statistics, or related field; experience in managementBachelor's in data science, statistics, or related field; technical proficiency
Work EnvironmentTypically in corporate offices, leading teamsOften in office or remote, focused on individual analysis tasks

The main difference between a Data Analysis Manager and a Data Analyst lies in scope and responsibilities. The manager oversees teams and strategic projects, while the analyst focuses on executing data analysis tasks. Both roles require strong analytical skills and relevant qualifications, but the manager's role emphasizes leadership and project management.

What are the most commonly searched types of Data Analysis jobs in Alberta? The most popular types of Data Analysis jobs in Alberta are:
What are popular job titles related to Data Analysis Manager jobs in Alberta? For Data Analysis Manager jobs in Alberta, the most frequently searched job titles are:
What job categories do people searching Data Analysis Manager jobs in Alberta look for? The top searched job categories for Data Analysis Manager jobs in Alberta are:
What cities in Alberta are hiring for Data Analysis Manager jobs? Cities in Alberta with the most Data Analysis Manager job openings:

Data Analyst / Data Product Analyst - DPA#4646

NavitasPartners

Sherwood Park, AB โ€ข On-site

Other

Posted 10 days ago


Job description

Job Title: Data Analyst / Data Product Analyst

Location: Edmonton, Alberta, Canada

Contract Duration:

  • 9 Months

Schedule:

  • Monday-Friday
  • 08:15 AM - 04:30 PM Alberta Time
Job Summary

"Navitas Partners, LLC" is seeking an experienced Data Analyst / Data Product Analyst to support a major regulatory modernization initiative focused on environmental and natural resource regulatory processes. This role will contribute to building scalable, secure, and analytics-ready data solutions that support regulatory oversight, compliance monitoring, and enterprise data initiatives.

The ideal candidate will possess strong expertise in Microsoft Azure data services, data engineering, analytics, and cloud-based data architecture while working within modernized enterprise data management environments.

Key Responsibilities
  • Design and implement scalable, secure, and high-performance Azure-based data architecture
  • Develop and maintain data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
  • Manage structured storage and data lake solutions using Azure Data Lake Storage Gen2
  • Integrate data from multiple enterprise systems, APIs, ServiceNow, and geospatial platforms
  • Build and optimize data workflows using Python and SQL for cleansing, transformation, and analytics
  • Develop semantic layers and data models supporting reporting, analytics, and AI initiatives
  • Design and expose secure APIs and data services using Azure API Management
  • Implement data governance, lineage tracking, metadata management, and classification standards
  • Ensure compliance with privacy and security standards including role-based access controls and encryption
  • Monitor, troubleshoot, and optimize data pipelines and platform performance
  • Utilize AI-assisted tools for automation, testing, monitoring, documentation, and code optimization
  • Design high-quality standardized datasets for advanced analytics and future AI use cases
Required Skills & Experience
  • Strong experience with Microsoft Azure cloud data platforms
  • Hands-on expertise with:
    • Azure Data Factory
    • Azure Databricks
    • Azure Synapse Analytics
    • Azure Data Lake Storage Gen2
    • Azure API Management
  • Advanced SQL and Python development experience
  • Experience building scalable ETL/ELT pipelines and cloud-native data architectures
  • Experience integrating enterprise systems, APIs, and structured/unstructured datasets
  • Strong knowledge of data governance, metadata management, and lineage tracking
  • Familiarity with privacy and compliance standards such as FOIP and GDPR
  • Experience supporting analytics, operational reporting, and machine learning data environments
  • Strong troubleshooting, analytical, and communication skills
Preferred Experience
  • Experience supporting government or regulatory modernization programs
  • Experience with geospatial data platforms and integrations
  • Exposure to AI-assisted development and automation tools
  • Experience in hybrid cloud and enterprise data environments