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Home Based Remote Data Labelling Jobs in Alberta

Enverus

Data Specialist - 26327D

Calgary, AB · Remote

We are currently recruiting for a Data Specialist to join our organization; this is a remote position based in the Canada. The Data Specialist plays a critical role in ensuring highquality data ...

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Home Based Remote Data Labelling information

What is the difference between Home Based Remote Data Labelling vs Home Based Remote Data Entry?

AspectHome Based Remote Data LabellingHome Based Remote Data Entry
CredentialsBasic computer skills, attention to detailBasic computer skills, accuracy
Work EnvironmentOnline platforms, flexible hoursOnline or offline, flexible hours
Industry UsageAI training, machine learningAdministrative, record keeping
Search & Comparison IntentUnderstanding data annotation rolesUnderstanding data input roles

Home Based Remote Data Labelling involves annotating or tagging data for AI and machine learning projects, requiring attention to detail and familiarity with online tools. In contrast, Home Based Remote Data Entry focuses on inputting data into systems, emphasizing accuracy and speed. Both roles are flexible, online-based, and in demand, but they serve different industry needs and skill sets.

What cities in Alberta are hiring for Home Based Remote Data Labelling jobs?

Cities in Alberta with the most Home Based Remote Data Labelling job openings:

Home Based Remote Data Labelling jobs near you
Infographic showing various Home Based Remote Data Labelling job openings in Alberta as of July 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 100% Remote job distribution.

Data Engineer - Senior (REMOTE) JP989

P@thlion Staffing Careers

Edmonton, AB • Remote

Full-time

Posted 9 days ago

Job description

Project Name:

Digital Regulatory Assurance System

Scope:

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.

This position 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.

Duties:

  • Collaborate with business stakeholders and product owners to understand data product objectives, requirements, and success criteria
  • 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 and downstream consumption.
  • 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.
  • Other duties as needed