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Data Analytics Engineer Jobs in Alberta (NOW HIRING)

Connect First Credit Union

Data Architect

Edmonton, AB · Remote

The Data Architect undertakes tasks such as data analysis, modeling, masking, provisioning, and ... Azure Data Engineer Associate) * Architecture Process Training or Certifications (eg: TOGAF ® ...

Finning

Data Engineer II

Edmonton, AB · Hybrid

Data is deeply embedded in the product, engineering, analytics, and operational culture at Finning. Finning Canada is looking to hire a permanent, fulltime Data Engineer II based either in Surrey ...

Finning

Data Engineer II

Calgary, AB · Hybrid

Data is deeply embedded in the product, engineering, analytics, and operational culture at Finning. Finning Canada is looking to hire a permanent, fulltime Data Engineer II based either in Surrey ...

10 Percent Recruiting Ltd.

Data Engineer

Edmonton, AB · Hybrid

Work with Business Analysts and Developers to ensure the warehouse contains the appropriate data models to handle both analytical and system reporting needs. Ensure that data security and access to ...

Clio

Data Architect

Calgary, AB · Hybrid

CA$128K - CA$147K/yr

Partner with Data Engineering, RevOps, Product, and other stakeholders to set modeling standards, manage dbt access and ownership boundaries, and enable "self-service" analytics across the company.

Viridien

Machine Learning Engineer

Calgary, AB · On-site

Machine Learning Engineer Calgary, AB, Canada Full-time Company Description Viridien is a global ... Data Analytics * Quantitative Analysis * Web Scraping * Model Development Responsibilities:

360training

Data Engineer (Canada)

Calgary, AB · Remote

Data Engineer We are seeking an experienced and skilled Data Engineer to join our innovative and ... Strong analytical and problem-solving skills, with a passion for continuous learning and innovation.

BBA, Inc.

Junior Advisor, Asset Analytics

Calgary, AB · On-site

... analysis and commissioning, asset management and analyticsand advisory services. BBA has a new ... BBA's PAAM teamis an advanced multidisciplinary group of engineers, data scientists,economists ...

BBA, Inc.

Junior Advisor, Asset Analytics

Edmonton, AB · On-site

... analysis and commissioning, asset management and analyticsand advisory services. BBA has a new ... BBA's PAAM teamis an advanced multidisciplinary group of engineers, data scientists,economists ...

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All JobsData Analytics Engineer JobsData Analytics Engineer Jobs in Alberta

Data Analytics Engineer information

See Alberta salary details

$65.5K

$117.1K

$197.5K

How much do data analytics engineer jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data analytics engineer in Alberta is $117,079.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,500.00 and $132,000.00 per year, depending on experience, location, and employer.

How do Data Analytics Engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

Can a data engineer make 200k?

Data engineers can earn $200,000 or more annually, especially with experience, advanced skills in cloud platforms, big data tools, and certifications. Salaries vary by location, industry, and company size, with senior roles and those in high-demand markets more likely to reach or exceed this level.

What engineers make $500,000?

Senior data analytics engineers with extensive experience, advanced skills in data modeling, machine learning, and proficiency with tools like Python, SQL, and cloud platforms can reach salaries of $500,000 or more, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership roles, and sometimes equity compensation.

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

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

Is 40 too late for data science?

Data Analytics Engineers and data science professionals can successfully transition into the field at age 40 or older, as skills such as programming, statistical analysis, and experience with tools like Python or SQL are valuable regardless of age. Many employers value diverse experience and lifelong learning, and certifications or online courses can help enhance credentials at any age.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What does a data analytics engineer do?

A data analytics engineer designs, builds, and maintains data pipelines and systems to collect, process, and analyze large datasets. They use tools like SQL, Python, and cloud platforms to enable data-driven decision-making and often collaborate with data scientists and business teams to deliver actionable insights.
What are the most commonly searched types of Data Analytics Engineer jobs in Alberta? The most popular types of Data Analytics Engineer jobs in Alberta are:
What are popular job titles related to Data Analytics Engineer jobs in Alberta? For Data Analytics Engineer jobs in Alberta, the most frequently searched job titles are:
What job categories do people searching Data Analytics Engineer jobs in Alberta look for? The top searched job categories for Data Analytics Engineer jobs in Alberta are:
Data Analytics Engineer jobs near you
Infographic showing various Data Analytics Engineer job openings in Alberta as of July 2026, with employment types broken down into 1% Internship, 91% Full Time, 6% Part Time, and 2% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $117,079 per year, or $56.3 per hour.

Data Analyst (Remote) JP927

P@thlion Staffing Careers

Edmonton, AB • Remote

Full-time

Re-posted 24 days ago

Job description

Project Name:
Digital Regulatory Assurance System
Job Title: Data Product Analyst
Scope:
The Data Product Analyst – Intermediate will primarily support the Digital Regulatory Assurance System (DRAS) program, where high quality, timely analytics are essential to regulatory and compliance functions. As data and analytics maturity increases, the role may be expanded to support additional enterprise data initiatives.
Modernization initiatives across the Government of Alberta are fundamentally changing how ministry users collect, manage, analyze, and use data as legacy systems are transformed into modern Data Management and Geospatial Platforms. This shift requires dedicated analytical capacity to ensure that the value of modernized data assets is fully realized.
DRAS is a Government of Alberta regulatory transformation initiative led by Environment and Protected Areas (EPA) to modernize, digitize, and streamline environmental and natural resource regulatory processes. DRAS supports the full regulatory lifecycle, from application and authorization to monitoring, compliance, remediation, and closure through a single, consolidated digital platform
As DRAS development continues, the volume, variety, and complexity of structured data continue to grow, creating a sustained need for dedicated data engineering and data product expertise. The Data Product Analyst role is critical to ensuring that modernization delivers tangible business value. This role will design, build, and operate reliable data pipelines that ingest and integrate data into the DMP, apply standardized transformations, enforce data quality and governance controls, and produce trusted, analytics‑ready datasets that support regulatory oversight, compliance monitoring, and evidence‑based decision‑making aligned with DRAS objectives.
Duties:
Design and implement scalable, secure, and high-performance data architecture on Microsoft Azure, supporting both cloud-native and hybrid environments.
Lead the development of data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
Work with the Data Architect and manage data lakes and structured storage solutions using Azure Data Lake Storage Gen2, ensuring efficient access and governance.
Integrate data from diverse source systems including ServiceNow, and geospatial systems, using APIs, connectors, and custom scripts.
Develop and maintain robust data models and semantic layers to support operational reporting, analytics, and machine learning use cases.
Build and optimize data workflows using Python and SQL for data cleansing, enrichment, and advanced analytics within Azure Databricks.
Design and expose secure data services and APIs using Azure API Management for downstream systems.
Implement data governance practices, including metadata management, data classification, and lineage tracking.
Ensure compliance with privacy and regulatory standards (e.g., FOIP, GDPR) through role-based access controls, encryption, and data masking.
Monitor and troubleshoot data pipelines and integrations, ensuring reliability, scalability, and performance across the platform.
Utilize AI and automation tools to streamline data engineering workflows, including pipeline development, testing, monitoring, and documentation.
Leverage AI-assisted tools for code generation, optimization, and review to improve development efficiency and code quality.
Design and curate standardized, high‑quality datasets that are suitable for advanced analytics and future AI use cases.