Senior Data Engineer - Oil & Gas
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 22h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 204d ago
The date the source published, not the day we noticed it (2026-03-22). Last seen at its source just now.
We have tracked this listing since 8 Oct 2026 (4 days). The employer's own board has carried it every time we have read it, most recently just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Canada
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay CA$156.6k–198.8k/yr
Middle 50% of 189 listings that do state pay — Engineering · Senior · Canada · CAD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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BambooHR employer's own board first seen 4d ago · last seen just now
The listing
We are looking for a Senior Databricks Data Engineer to join our team.
This role is ideal for someone who enjoys building end-to-end solutions 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
- Implementing Azure Databricks foundations and pipelines with best practices
- 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.
- Complex Excel and CSV integration experience
- Experience with Databricks dashboards and Genie
- Strong requirements gathering experience with Field Engineers and Technical SMEs
- Demonstrated experience with incremental processing, change detection, reconciliation, and pipeline design
- Experience handling time-series, telemetry, sensor, operational, scientific or industrial data
- Medallion architecture and modelling
- 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.
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!