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Analytics Engineer Jobs in Alberta (NOW HIRING)

Krux Analytics

Data Architect

Calgary, AB · On-site

Monitor and improve analytical database performance in cloud environments * Conduct model tuning ... Bachelors Degree (Computer Science, Technology, Engineering, or related field) * 10+years of ...

Acero Engineering Inc.

Electrical Engineer

Edmonton, AB · On-site

$65 - $95/hr

Acero Engineering Inc. ("Acero"), a growing oil & gas facilities engineering company providing ... Circuit analyses, DC and AC loss calculations, cable ampacity studies, and AC/DC cable sizing.

Associated Engineering

Bridge Engineer

Edmonton, AB · Hybrid

Associated Engineering, a proudly Canadian, Employee-owned Company is seeking an experienced and ... Leading the structural team in analysis, design, evaluation and preparation of calculations and ...

Royal Bank of Canada

Staff Software Engineer

Calgary, AB · On-site

... Analytics, Programming Languages, Python (Programming Language), Red Hat OpenShift Additional Job Details Address: 407 8 AVE SW:CALGARY City: Calgary Country: Canada Work hours/week: 37.5 Employment ...

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Analytics Engineer information

What do analytics engineers do?

Analytics engineers design, build, and maintain data pipelines and infrastructure to enable data analysis and reporting. They work with tools like SQL, Python, and data warehouses to ensure data is accurate, accessible, and well-structured for analysts and data scientists. Their role often involves collaborating with teams to optimize data workflows and ensure data quality.

What are the key skills and qualifications needed to thrive as an analytics engineer, and why are they important?

To thrive as an Analytics Engineer, you need a strong foundation in data modeling, SQL, and analytics engineering principles, often supported by a degree in computer science, data science, or a related field. Proficiency with data transformation tools such as dbt, cloud data warehouses like Snowflake or BigQuery, and version control systems like Git is essential. Strong problem-solving skills, communication, and collaboration abilities help translate business needs into scalable data solutions and foster teamwork. These skills and qualities are crucial for ensuring data quality, building reliable analytics infrastructure, and enabling data-driven decision-making across organizations.

What is the difference between Analytics Engineer vs Data Engineer?

AspectAnalytics EngineerData Engineer
CredentialsOften requires SQL, Python, data modeling certificationsRequires similar skills, often with additional focus on infrastructure and systems
Work EnvironmentFocuses on data analysis, visualization, and reportingBuilds data pipelines, manages data infrastructure
Industry UsageCommon in analytics teams, BI, and data-driven rolesPrevalent in data engineering, data platform teams

While both roles work closely with data, Analytics Engineers primarily focus on transforming data for analysis and visualization, whereas Data Engineers build the infrastructure and pipelines that enable data access. Understanding these differences helps in choosing the right career path or job role.

How does an analytics engineer typically collaborate with data scientists and business stakeholders on projects?

Analytics Engineers play a critical bridge role between data engineering and data analysis. They work closely with data scientists to transform raw data into clean, reliable datasets that are ready for advanced analytics or modeling. At the same time, they collaborate with business stakeholders to understand reporting needs, ensuring that data models align with business goals. Regular communication and iterative feedback are key, as Analytics Engineers often gather requirements, build data pipelines, and adjust data products based on stakeholder input.

What is an analytics engineer?

An Analytics Engineer is a professional who bridges the gap between data engineering and data analysis. They are responsible for designing, building, and maintaining data models, pipelines, and analytics tools that enable organizations to make data-driven decisions. Analytics Engineers often work closely with data analysts and business stakeholders to ensure clean, reliable, and well-structured data is available for reporting and analysis. Their work typically involves using SQL, data transformation tools like dbt, and cloud data warehouses to create scalable and efficient data solutions.
What job categories do people searching Analytics Engineer jobs in Alberta look for? The top searched job categories for Analytics Engineer jobs in Alberta are:
Analytics Engineer jobs near you
Infographic showing various Analytics Engineer job openings in Alberta as of August 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 81% In-person, and 19% Remote job distribution.

Senior Azure Databricks Engineer (4 days onsite - Calgary)

Hire DigITalent

Calgary, AB • Hybrid

Full-time

Posted 10 days ago

Job description

Senior Azure Databricks Engineer (Contract)

Location: Calgary, AB (Onsite 4 days/week – downtown)

Term: 3–6 months

Engagement: Contract through a consulting firm delivering data engineering projects for multiple enterprise clients Industry: Oil & Gas (experience a plus, not required)

A leading Canadian consulting firm is seeking an experienced Databricks Engineer to support several high-impact client initiatives. This is a hands-on engineering role focused on designing, building, and optimizing data workloads on Azure Databricks, including large-scale migrations, platform modernization, and AI/ML enablement.

You will work directly with consulting delivery teams and enterprise client stakeholders in a fast-moving, collaborative environment. This role requires Calgary-based candidates who are comfortable being onsite 4 days per week.

Key Responsibilities

  • Design and develop Azure Databricks solutions using PySpark and SQL
  • Build and optimize batch and streaming data pipelines
  • Migrate legacy workloads from Synapse and other platforms into Databricks
  • Implement Delta Lake patterns (medallion architecture, CDC, data quality)
  • Integrate Databricks with Azure services such as ADLS, ADF, Azure Key Vault, and Azure DevOps/GitHub
  • Optimize performance and cost (cluster sizing, job orchestration, query tuning)
  • Collaborate with architects, analytics engineers, and client teams
  • Contribute to reusable accelerators, internal standards, and best practices
  • Support client enablement through documentation and knowledge transfer
  • Implement governance models and Unity Catalog (access controls, lineage, security frameworks)

Required Skills & Experience

  • 7+ years of data engineering experience
  • 4+ years of recent, hands-on Databricks engineering (Delta Lake, Unity Catalog, MLflow)
  • Strong experience building production pipelines using PySpark and SQL
  • Proven experience with Azure data services: ADLS, ADF, Synapse, Key Vault, Azure DevOps
  • Experience leading large-scale data migrations and ETL/ELT workload modernization
  • Strong understanding of lakehouse architecture, medallion patterns, and modern ELT/ETL approaches
  • Experience implementing CI/CD for data workloads
  • Strong understanding of enterprise data governance, security, and compliance
  • Excellent communication skills with comfort working directly with client teams
  • Previous consulting or professional services experience strongly preferred
  • Databricks and Azure certifications considered an asset

Ideal Candidate Profile

A strong, hands-on Azure Databricks engineer who:

  • Delivers high-quality, production-ready code
  • Understands modern data architecture and platform design
  • Communicates clearly with technical and non-technical stakeholders
  • Thrives in collaborative, client-facing environments
  • Is comfortable being onsite 4 days/week
  • Can contribute immediately to high-impact migration and modernization projects

Why This Role Stands Out

  • Contract through a delivery-focused consulting firm supporting multiple enterprise clients
  • Work on high-impact Oil & Gas data initiatives (migration, modernization, AI/ML enablement)
  • Exposure to advanced Databricks features and architecture patterns

Only qualified candidates will be contacted for next steps. Thank you in advance for your interest.