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Data Handling Jobs in Alberta (NOW HIRING)

Architect and implement robust data infrastructure capable of handling high-volume data ingestion and processing . * Develop and manage our central data warehouse in Google BigQuery . * Design and ...

Enable secure OT and AI adoption by providing guidance on segmentation, monitoring, data handling and third-party risk. * Advance risk and compliance by developing standards, support policies ...

Implement data handling and automation improvements * Configuring systems to meet engineering standards, including GIS, and classification requirements * Develop and deliver training on tool ...

Implement data handling and automation improvements * Configuring systems to meet engineering standards, including GIS, and classification requirements * Develop and deliver training on tool ...

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Data Handling information

What is the difference between Data Handling vs Data Analysis?

AspectData HandlingData Analysis
Required SkillsData collection, storage, organizationData interpretation, insights, reporting
ToolsDatabases, spreadsheets, data management softwareStatistical tools, visualization software
Work EnvironmentData entry, database management, data cleaningData interpretation, decision support, reporting
Industry UsageIT, healthcare, finance, logisticsMarket research, business intelligence, finance

Data Handling focuses on collecting, storing, and organizing data, ensuring data quality and accessibility. Data Analysis involves interpreting this data to generate insights, trends, and reports that inform business decisions. While Data Handling ensures data integrity, Data Analysis transforms data into actionable information.

Infographic showing various Data Handling job openings in Alberta as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution.

Senior Data Engineer - Oil & Gas

Calgary, AB

Contractor

Re-posted 8 days ago


Job description

We are looking for a Senior AI/ML Engineers to join our team, that sit at the intersection of software engineering, data engineering, and applied AI.



This role is ideal for someone who enjoys building end-to-end solutions - from data pipelines and backend systems to AI-powered applications - and wants to work on real-world industrial use cases that comes from an Engineering background, and has experience in Oil & Gas.


In this position, you'll contribute to a variety of impactful client projects, including:


  • Building AI-powered anomaly detection systems for operational and industrial data
  • Modernizing asset management workflows
  • Developing natural language interfaces for querying enterprise and operational data
  • Designing and implementing AI agents and copilots for business users
  • Creating scalable data pipelines and ML workflows in cloud environments
  • Enabling real-time and batch data processing for analytics and AI use cases
  • Working through complex and challenging data conditions, including incremental processing, late-arriving or changing records, complex business and engineering rules, and reconciliation of results across processing runs


The ideal candidate combines strong data engineering experience with an engineering or applied-science background. Direct experience industrial time-series data, scientific measurements, telemetry, financial reconciliation, or other datasets where accuracy, traceability, and incremental recalculation are critical.


Key Responsibilities


  • Design, build, and optimize pipelines for sensor and related operational data.
  • Develop complex transformation and calculation logic based on engineering requirements.
  • Implement robust incremental-processing patterns for high-volume and continuously changing datasets.
  • Design, build, and deploy end-to-end AI/ML solutions in production environments
  • Develop robust backend systems and APIs to support AI-driven applications
  • Build and maintain data pipelines and feature engineering workflows
  • Implement and operationalize machine learning models (training, deployment, monitoring)
  • Work with modern AI tooling (LLMs, agents, orchestration frameworks)
  • Collaborate with clients to translate business problems into technical solutions
  • Contribute to architecture decisions and best practices across projects
  • Mentor client team members and contribute to internal capability building
  • Work directly with engineering and operational SMEs to understand physical processes and translate their knowledge into technical requirements.
  • Make engineering calculations and data transformations explainable, traceable, testable, and auditable.
  • Document data lineage, calculation logic, assumptions, dependencies, and exception-handling rules.


Ideal Background

  • Senior-level experience designing and developing production data pipelines with Azure Databricks including strong experience with complex SQL, Python, Spark, or comparable data-processing technologies.
  • Demonstrated experience with incremental processing, change detection, reconciliation, and idempotent pipeline design.
  • Experience handling time-series, telemetry, sensor, operational, scientific, or industrial data.
  • Ability to work through ambiguous requirements with highly specialized SMEs.
  • Strong analytical and investigative skills, with the patience to work through detailed logic and difficult data-quality problems.
  • Degree or professional background in petroleum, reservoir, chemical, mechanical, geological, geophysical, or another relevant engineering or applied-science discipline is strongly preferred.
  • Experience in upstream oil and gas, thermal operations, SAGD, well surveillance, production engineering, or subsurface data would be a significant asset.
  • Hands-on experience with AI/ML workflows and model deployment.
  • Modern developer tooling (Cursor, AI-assisted development, Langraph, "vibe coding").



    This role presents an exciting opportunity to work on practical, high-impact AI use cases - not just prototypes, shape how AI is applied to client environments, and change the game on traditional processes and platforms.Come join a growing organization helping clients take a new, lean and value-driven approach to data and engineering!