Senior Data Engineer - Oil & Gas

Data Elephant; Calgary; On-site; CAD 120,000 - 180,000; Full time

Job summary

Data Elephant is seeking a Senior AI/ML Engineer to join a cross-disciplinary team working at the intersection of software, data, and applied AI. You will build end-to-end data pipelines and AI-powered applications for industrial use cases, with emphasis on time-series, telemetry, and complex data processing in the Oil & Gas sector.

The role requires strong data engineering skills, experience with Azure Databricks, SQL, Python, and Spark, and the ability to translate SME insights into technical

Qualifications

• Senior-level experience designing and developing production data pipelines.
• Strong SQL, Python, and Spark experience for big data processing.
• Hands-on experience with Azure Databricks and modern AI tooling.
• Experience handling industrial time-series or telemetry data is a plus.

Responsibilities

• Design, build, and optimize data pipelines for sensor and operational data.
• Develop transformation and calculation logic based on engineering requirements.
• Implement incremental-processing patterns for high-volume 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.
• Train, deploy, and monitor ML models.

Skills

Azure Databricks

SQL

Python

Spark

Data pipelines

Education

Engineering degree (petroleum/oil & gas)

Tools

Azure Databricks

Cursor

Langraph

vibe coding

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!