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Remote Data Engineer Jobs in Quebec (NOW HIRING)

Omm IT Solutions

It is a 100% Remote position in Canada. \n * Candidates Preferred from EST\/CST Time Zone. \n JOB ... Architect and implement Fabric solutions for data engineering (Spark), Data Factory pipelines , and ...

YO AI Labs

Mental Health Expert - Remote

Montreal, QC · Remote

$200 - $350/hr

AI/ML Engineer Job Type: Full-Time Location: Remote Job Summary We are seeking an experienced AI/ML ... Build robust ETL and data pipelines , metadata catalogs, and ontologies for AI training and ...

Targeted Talent

Senior QA Engineer

Montreal, QC · Remote

This is a permanent position that is completely remote! Our client is a fintech company based out ... You'll have the opportunity to work on an amazing platform that combines Data Analytics, and ...

Targeted Talent

Senior QA Engineer

Quebec, QC · Remote

This is a permanent position that is completely remote! Our client is a fintech company based out ... You'll have the opportunity to work on an amazing platform that combines Data Analytics, and ...

YO AI Labs

AI coding agents Expert - Remote

Montreal, QC · Remote

$80 - $120/hr

Senior Software Engineer Job Type: Contractor (~15 hours/week) Location: Remote Job Summary We are ... Strong understanding of algorithms and data structures . * Experience with bug fixing and debugging ...

Targeted Talent

Senior QA Engineer

Quebec, QC · Remote

This is a permanent position that is completely remote! Our client is a fintech company based out ... You'll have the opportunity to work on an amazing platform that combines Data Analytics, and ...

Targeted Talent

Senior QA Engineer

Montreal, QC · Remote

This is a permanent position that is completely remote! Our client is a fintech company based out ... You'll have the opportunity to work on an amazing platform that combines Data Analytics, and ...

YO AI Labs

AI Trainer - Remote

Montreal, QC · Remote

$80 - $120/hr

Senior Software Engineer Job Type: Contractor (~15 hours/week) Location: Remote Job Summary We are ... Strong understanding of algorithms and data structures . * Experience with bug fixing and debugging ...

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Showing results 41-60

All JobsData Engineer JobsRemote Data Engineer JobsRemote Data Engineer Jobs in Quebec

Remote Data Engineer information

See Quebec salary details

$68.5K

$127.3K

$162K

How much do remote data engineer jobs pay per year?

As of Sep 1, 2026, the average yearly pay for remote data engineer in Quebec is $127,317.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,000.00 and $145,000.00 per year, depending on experience, location, and employer.

What is a remote data engineer?

A Remote Data Engineer is a professional who designs, builds, and maintains data pipelines, databases, and data processing systems while working from a location outside of a traditional office. They collaborate with data scientists, analysts, and other stakeholders to ensure data is collected, stored, and made accessible efficiently and securely. Remote Data Engineers use programming languages like Python or Scala, work with technologies such as SQL, Hadoop, or cloud platforms, and address challenges related to data quality and scalability. Their remote role allows them to work for companies regardless of geographic location, often relying on virtual collaboration tools to stay connected with their teams.

What does a remote data engineer do?

As a remote data engineer, you focus on collecting, storing, and organizing large amounts of information. You work from home to design, develop, and maintain systems for the mining, warehousing, and processing of data. A data engineer communicates with employers, clients, or other data professionals to assess the needs of the project and develop and implement solutions to meet those needs. Data engineers also take steps to manage current database architecture and make updates when needed. Remote engineers typically handle their responsibilities in a cloud-based environment using “big data” tools, such as Amazon Web Services (AWS) and SQL.

What are the key skills and qualifications needed to thrive as a remote data engineer, and why are they important?

To thrive as a Remote Data Engineer, you need strong programming skills in languages like Python or Scala, expertise in SQL, data modeling, and a background in computer science or a related field. Familiarity with cloud platforms (such as AWS, Azure, or GCP), big data tools (like Hadoop and Spark), and certifications in cloud or data engineering are highly valued. Excellent problem-solving, communication, and self-management skills help remote data engineers collaborate effectively and stay productive in a distributed environment. These competencies ensure reliable data pipelines, scalable solutions, and seamless teamwork, which are critical for organizational success in data-driven projects.

How do remote data engineers typically collaborate with other team members across different time zones?

Remote Data Engineers often work with cross-functional teams, including data scientists, analysts, and software engineers, many of whom may be located in different parts of the world. Collaboration is usually facilitated through project management tools, version control platforms, and regular virtual meetings. It’s common to have a mix of synchronous check-ins and asynchronous communication, allowing for flexible scheduling and efficient handoffs. Strong written communication skills and proactive status updates are essential for staying aligned with team objectives and project deadlines.

What is the difference between Remote Data Engineer vs Remote Data Analyst?

AspectRemote Data EngineerRemote Data Analyst
Required CredentialsBachelor's in CS, Data Science, or related; SQL, Python, cloud certificationsBachelor's in Statistics, Data Science, or related; SQL, Excel, visualization tools
Work EnvironmentCollaborates with data engineering teams, cloud platforms, big data toolsWorks with business teams, dashboards, reporting tools
Industry UsageTech, finance, healthcare, e-commerceMarketing, finance, retail, healthcare
Common Search IntentBuilding data pipelines, data infrastructureData reporting, insights, visualization

Remote Data Engineers focus on designing and maintaining data pipelines and infrastructure, often requiring programming and cloud skills. Remote Data Analysts interpret data, create reports, and provide insights using visualization tools. While both roles work with data, their responsibilities and skill sets differ, making each suited for different career paths within data teams.

Are remote data engineers still in demand?

Remote data engineers are currently in high demand due to the increasing reliance on data-driven decision making and cloud-based data platforms. Skills in SQL, Python, cloud services, and data pipeline tools are highly sought after, and many organizations continue to hire for remote roles to access a broader talent pool.

Can a remote data engineer work remotely?

Yes, remote data engineers can work remotely, as the role primarily involves managing data pipelines, databases, and cloud-based tools that can be accessed from anywhere with an internet connection. Many companies offer remote positions for data engineers, often requiring skills in SQL, Python, cloud platforms, and data architecture. However, some roles may require occasional on-site presence or specific certifications depending on the employer's policies.
More about Remote Data Engineer jobs

What are the most commonly searched types of Data Engineer jobs in Quebec?

The most popular types of Data Engineer jobs in Quebec are:

What are popular job titles related to Remote Data Engineer jobs in Quebec?

For Remote Data Engineer jobs in Quebec, the most frequently searched job titles are:

What job categories do people searching Remote Data Engineer jobs in Quebec look for?

The top searched job categories for Remote Data Engineer jobs in Quebec are:

What cities in Quebec are hiring for Remote Data Engineer jobs?

Cities in Quebec with the most Remote Data Engineer job openings:

Remote Data Engineer jobs near you
Infographic showing various Remote Data Engineer job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $127,317 per year, or $61.2 per hour.

Consulting Senior Data Architect (Microsoft Fabric Focus)

Omm IT Solutions

Remote

Contractor

Re-posted 2 days ago

Job description

\n <\/head>\n \n

PLEASE NOTE:<\/b><\/span><\/span><\/u> <\/b><\/span><\/span>
<\/b><\/span><\/p>\n

    \n
  • It is a 100% Remote position in Canada.<\/b><\/span><\/span>
    <\/b><\/span><\/span><\/li>\n
  • Candidates Preferred from EST\/CST Time Zone.<\/b><\/span><\/span>
    <\/b><\/span><\/span><\/li>\n <\/ul>

    JOB SUMMARY:<\/b><\/span><\/span><\/u>
    <\/span><\/span><\/p>\n

    \n We are seeking a Consulting Senior Data Architect with deep, production\-grade Microsoft Fabric experience to accelerate our active rollout of Fabric for customer\-facing and revenue\-impacting digital products hosted in Azure. This is a hands\-on role responsible for designing and building Fabric artifacts (pipelines, lakehouses, eventstream\/real\-time patterns), defining data architecture standards (conceptual\/logical\/physical), and implementing governance and security guardrails that scale across multiple product teams.<\/span><\/span>
    \n <\/div>\n
    \n
    \n <\/div>\n
    \n Key Responsibilities:<\/b><\/span><\/u><\/span>
    \n <\/div>

    Microsoft Fabric Enablement (Hands\-on Delivery + Standardization)<\/b><\/span><\/span>
    <\/span><\/p>\n

      \n
    • Architect and implement Fabric solutions for data engineering (Spark), Data Factory pipelines<\/b>, and real\-time analytics \/ Event streams <\/b>aligned to digital product needs.<\/span><\/span>
      <\/span><\/li>\n
    • Build and standardize Fabric patterns for ingestion, transformation, and serving across workloads including operational analytics, near\-real\-time, batch, and data science\/ML.<\/b><\/span><\/span>
      <\/span><\/li>\n
    • Create repeatable reference implementations for common digital product scenarios (IoT telemetry, time series, transactional + event fusion, documents, geospatial).<\/span><\/span>
      <\/span><\/li>\n <\/ul>

      Unified Data Platform Architecture (Target State + Roadmap)<\/b><\/span><\/span>
      <\/span><\/p>\n

        \n
      • Produce and maintain the target\-state architecture<\/b> for Fabric\-based data platform capabilities.<\/span><\/span>
        <\/span><\/li>\n
      • Define domain\-oriented data product patterns<\/b> including how shared\/enterprise datasets are curated and reused.<\/span><\/span>
        <\/span><\/li>\n
      • Establish architectural boundaries and integration guidance for shared datasets vs. product\-owned datasets.<\/span><\/span>
        <\/span><\/li>\n <\/ul>

        Data Modeling Standards (Conceptual \/ Logical \/ Physical)<\/b><\/span><\/span>
        <\/span><\/p>\n

          \n
        • Define and enforce data modeling standards and templates appropriate to Fabric Lakehouse\/Warehouse patterns and product analytics needs.<\/span><\/span>
          <\/span><\/li>\n
        • Provide modeling guidance for high\-variance data types (telemetry, geospatial, documents) and hybrid operational\-analytics use cases.<\/span><\/span>
          <\/span><\/li>\n
        • Define standards for schema evolution, versioning, and contract\-first data interfaces (where applicable).<\/span><\/span>
          <\/span><\/li>\n <\/ul>

          Governance, Security, and Compliance by Design<\/b><\/span><\/span>
          <\/span><\/p>\n

            \n
          • Design and implement a governance model covering classification, retention, lineage,<\/b> and auditability.<\/span><\/span>
            <\/span><\/li>\n
          • Ensure compliance guardrails are built into delivery patterns and operational processes to meet GDPR, ISO 27001, and data residency<\/b> requirements.<\/span><\/span>
            <\/span><\/li>\n
          • Define and enforce Fabric access controls using Entra ID, RBAC,<\/b> and workspace\-level controls<\/b> (including guidance for separation of duties and least privilege).<\/span><\/span>
            <\/span><\/li>\n <\/ul>

            CI\/CD + Infrastructure as Code (IaC) for Fabric<\/b><\/span><\/span>
            <\/span><\/p>\n

              \n
            • Define and implement a CI\/CD approach for Fabric artifacts<\/b> as the enterprise source of truth.<\/span><\/span>
              <\/span><\/li>\n
            • Establish release patterns for Fabric changes (promotion strategy, environment separation, approvals, and quality gates) aligned to platform standards.<\/span><\/span>
              <\/span><\/li>\n
            • Manage Fabric\-related platform configuration using Terraform<\/b> as the IaC approach (including reusable modules\/patterns).<\/span><\/span>
              <\/span><\/li>\n
            • Create golden path templates and guidance that product teams can adopt with minimal friction.<\/span><\/span>
              <\/span><\/li>\n <\/ul>

              Capacity Planning, Cost Model, and Chargeback\/Show back<\/b><\/span><\/span>
              <\/span><\/p>\n

                \n
              • Design Fabric capacity strategy (SKU sizing, workload isolation, scaling model) to support multiple products reliably.<\/span><\/span>
                <\/span><\/li>\n
              • Define guardrails and operational practices that reduce waste and improve predictability.<\/span><\/span>
                <\/span><\/li>\n <\/ul>

                Reliability, Observability, and Operational Readiness<\/b><\/span><\/span>
                <\/span><\/p>\n

                  \n
                • Define reliability patterns and operational standards for data pipelines and real\-time workloads.<\/span><\/span>
                  <\/span><\/li>\n
                • Integrate logging\/monitoring with Log Analytics<\/b> and security monitoring with Sentinel,<\/b> including alerting and incident response considerations.<\/span><\/span>
                  <\/span><\/li>\n
                • Define and operationalize data quality SLAs<\/b> (freshness, completeness, accuracy, timeliness) and embed quality checks into delivery pipelines.<\/span><\/span>
                  <\/span><\/li>\n <\/ul>

                  Consulting Engagement + Governance Forums<\/b><\/span><\/span>
                  <\/span><\/p>\n

                    \n
                  • Participate in architectural governance and provide architecture review\/sign\-off, with authority to mandate standards<\/b> when necessary to protect platform integrity<\/span><\/span>
                    <\/span><\/li>\n
                  • Partner closely with platform engineering to align patterns across identity, network, DevOps, and security.<\/span><\/span>
                    <\/span><\/li>\n <\/ul>

                    Working Style & Mindset<\/b><\/span><\/span>
                    <\/span><\/p>\n

                      \n
                    • Hands\-on architect:<\/b> you can design <\/span>and<\/span><\/i> build the critical Fabric artifacts to prove patterns.<\/span><\/span>
                      <\/span><\/li>\n
                    • Platform\-oriented:<\/b> you think in reusable standards, templates, and repeatable governance.<\/span><\/span>
                      <\/span><\/li>\n
                    • Strong consultative presence:<\/b> you can advise product teams while also driving decisions and outcomes.<\/span><\/span>
                      <\/span><\/li>\n
                    • Comfortable with authority:<\/b> you can mandate standards when required to protect the platform and business.<\/span><\/span>
                      <\/span><\/li>\n
                    • Documentation discipline:<\/b> you produce clear ADRs, standards, and operating playbooks.<\/span><\/span>
                      <\/span><\/li>\n <\/ul>

                      Engagement & Collaboration<\/b><\/span><\/span>
                      <\/span><\/p>\n

                        \n
                      • Supports product teams through office hours<\/b> and project\-based sprints.<\/b><\/span><\/span>
                        <\/span><\/li>\n
                      • Works primarily with: Azure lead architect, security architect\/engineer, DevOps platform engineer, and product engineering teams.<\/span><\/span>
                        <\/span><\/li>\n
                      • Data ownership remains with product teams;<\/b> this role defines the how (standards\/patterns\/governance), not centralized ownership.<\/span><\/span>
                        <\/span><\/li>\n <\/ul>\n
                        \n
                        <\/span>\n <\/div><\/span>
                        Requirements<\/h3>

                        Required Deliverables:<\/b><\/span><\/u><\/span>
                        <\/p>

                        You will be accountable for producing the following:<\/span><\/span>
                        <\/span><\/p>\n

                          \n
                        • Target\-state architecture <\/b><\/span><\/span>
                          <\/span><\/li>\n
                        • Data model standards:<\/b> conceptual \/ logical \/ physical + templates<\/span><\/span>
                          <\/span><\/li>\n
                        • Domain\-oriented data product patterns<\/b> and operating guidance<\/span><\/span>
                          <\/span><\/li>\n
                        • Governance model:<\/b> classification, retention, lineage, access controls<\/span><\/span>
                          <\/span><\/li>\n
                        • Fabric workspace strategy + operating model<\/b> (environments, isolation, ownership, lifecycle)<\/span><\/span>
                          <\/span><\/li>\n
                        • CI\/CD approach for Fabric artifacts<\/b> integrated with platform guardrails<\/span><\/span>
                          <\/span><\/li>\n
                        • IaC approach using Terraform<\/b> for Fabric\-related configuration and<\/span><\/span>
                          <\/span><\/li>\n
                        • Cost model + capacity planning strategy<\/b> (SKU sizing, isolation, show back\/chargeback)<\/span><\/span>
                          <\/span><\/li>\n
                        • Architecture Decision Records (ADRs)<\/b> for key platform decisions<\/span><\/span>
                          <\/span><\/u><\/li>\n <\/ul>

                          Qualifications:<\/b><\/span><\/u><\/span>
                          <\/p>

                          Required Experience & Skills<\/b><\/span><\/span>
                          <\/span><\/p>\n

                            \n
                          • 10+ years<\/b> in data architecture\/data engineering roles, including platform\-scale design.<\/span><\/span>
                            <\/span><\/li>\n
                          • Proven Microsoft Fabric production implementations<\/b> <\/b>you have delivered Fabric solutions that run in production with real operational constraints.<\/span><\/span>
                            <\/span><\/li>\n
                          • Deep hands\-on expertise in Fabric areas central to our rollout:<\/span><\/span>
                            <\/span><\/li>\n
                              \n
                            • Data Engineering <\/b><\/span><\/span>
                              <\/b><\/span><\/li>\n
                            • Data Factory (pipelines)<\/b><\/span><\/span>
                              <\/b><\/span><\/li>\n
                            • Real\-time analytics \/ Event Streams<\/b><\/span><\/span>
                              <\/span><\/li>\n <\/ul>\n
                            • Strong architecture capability across mixed data types: transactional, telemetry\/events, documents, geospatial, time series.<\/span><\/span>
                              <\/span><\/li>\n
                            • Demonstrated experience implementing and governing:<\/span><\/span>
                              <\/span><\/li>\n
                                \n
                              • Data modeling standards (conceptual\/logical\/physical)<\/span><\/span>
                                <\/span><\/li>\n
                              • Data governance (classification, retention, lineage)<\/span><\/span>
                                <\/span><\/li>\n
                              • Security patterns using Entra ID<\/b>, RBAC, workspace\-level controls<\/span><\/span>
                                <\/span><\/li>\n
                              • Compliance guardrails for GDPR, ISO 27001,<\/b> and data residency<\/b><\/span><\/span>
                                <\/span><\/li>\n <\/ul>\n
                              • Strong DevOps fluency:<\/span><\/span>
                                <\/span><\/li>\n
                                  \n
                                • CI\/CD patterns and operational delivery<\/span><\/span>
                                  <\/span><\/li>\n
                                • Terraform<\/b> as mandatory IaC for repeatability and standardization<\/span><\/span>
                                  <\/span><\/li>\n <\/ul>\n
                                • Ability to define standards and enforce guardrails<\/b> while maintaining a delivery\-first, pragmatic approach.<\/span><\/span>
                                  <\/span><\/li>\n <\/ul>

                                  Preferred<\/u><\/b><\/span><\/span>
                                  <\/span><\/p>\n

                                    \n
                                  • Experience supporting IoT<\/b> and telemetry\-heavy product ecosystems.<\/span><\/span>
                                    <\/span><\/li>\n
                                  • Experience designing data quality frameworks and SLAs for operational analytics and near\-real\-time processing.<\/span><\/span>
                                    <\/span><\/li>\n
                                  • Familiarity integrating observability\/security signals into Log Analytics<\/b> and Sentinel.<\/b><\/span><\/span>
                                    <\/span><\/li>\n <\/ul>\n
                                    \n
                                    <\/span>\n <\/div><\/span>
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