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Added 13 mins ago
QI
Tech Lead
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- Full-time
Guide architecture, design, data modelling, and implementation choices within your team
Added 13 mins ago
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Lead responses for RFIs, RFPs, security reviews, and architecture assessments.
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Added 29 mins ago
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…deliver complex solutions that meet S3's rigorous standards for security, durability, availability, and…
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Buyerlink

Data Engineer

Victoria, BC, Canada

Compensation: $90,000-$120,000 CAD · Full-time

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Job details

Pay: Compensation: $90,000-$120,000 CAD
Job type: Full-time
Apply by: May 3, 2027
Posting language: English
Posted: Oct 10, 2026

Original job description

We’re looking for a modern Data Engineer who thrives at the intersection of data, cloud, and AI. You build the systems that power insight - scalable pipelines, cloud-native data platforms, and intelligent workflows that turn raw data into business value at speed. In this role, you’ll architect and own our data infrastructure end-to-end: designing pipelines, optimizing data warehouses on platforms like Snowflake and Redshift, and leveraging AI-assisted tooling and agentic development practices to move faster and smarter. You bring a builder’s mindset, are fluent in modern data stacks, and are energized by a rapidly evolving technology landscape.

This is a full-time, in-person role at our Victoria office.

What you’ll do:

Support the building of data acquisition and preparation processes
Creating data integration scripts and executing analytical queries that transform and utilize data for business insights
Data pipeline maintenance/testing
Facilitate data cleansing and data enrichment
Identify ways to improve data reliability, efficiency, and quality
Prepare data for predictive and prescriptive modeling
Support machine learning operations (MLOps)
Enable searching, data visualization, and advanced analytics - including building and maintaining dashboards and reports in Tableau and Power BI to drive self-serve analytics across the business
Design, build, and maintain a semantic layer of data - a governed, business-ready data foundation that serves as the trusted source for BI, reporting, and AI data agents
Work with stakeholders to understand needs for data structure, availability, and accessibility
Selecting and integrating any data tools and frameworks required to provide requested capabilities
Implement data security and protection practices
Designing and developing databases
Create technical documentation, such as user manuals, process flow diagrams, system architecture diagrams, etc.
Responsible for ensuring the accuracy and quality of data

What you bring:

EXPERIENCE/TRAINING/EDUCATION:

At least four years’ experience is preferred.

COMMUNICATION SKILLS:

Ability to read, analyze, and interpret general business periodicals, professional journals, technical procedures, or governmental regulations.
Ability to write reports, business correspondence, and procedure manuals.
Ability to effectively present information and respond to questions from groups of managers, clients, customers, and the general public.
Problem-solving, teamwork, and effective communication with both technical and non-technical audiences are crucial.

REASONING ABILITY:

High Skills:

Ability to solve practical problems and deal with a variety of concrete variables in situations where only limited standardization exists.
Ability to interpret a variety of instructions furnished in written, oral, diagram, or schedule form.

MATHEMATICAL SKILLS:

High Skills:

Ability to work with mathematical concepts such as probability and statistical inference, and fundamentals of plane and solid geometry and trigonometry.
Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations.

SKILLS/ABILITIES:

Knowledge of various ETL techniques and frameworks
Strong background with data modeling, data access, and data storage techniques
Proficiency in Application Development code like Python, C#, R or Java
Strong background to manage the entire back-end development life cycle of a data warehouse
Proficiency understanding of Data Store technologies like Relational Databases and NoSQL but not limited to those
Knowledge of cloud infrastructure and services like Azure, AWS, or GCP
Experience with integration of data from multiple data sources
Being able to set up and manage ETL tools and pipelines that support these projects
Proficiency understanding of distributed computing principles
Proficiency in BI and data visualization tools including Tableau and Power BI
Experience with Amazon Redshift and cloud data warehouse technologies
Experience with Snowflake, including Snowflake Cortex for AI-powered analytics and data processing
Experience applying AI tools and agentic development practices to data engineering workflows - including LLM-assisted pipeline development, AI-powered code generation, and autonomous agent orchestration

Compensation: $90,000-$120,000 CAD

Compensation will be determined by factors including knowledge and skills, role-specific qualifications, market location, and experience.

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More about this job

Responsibilities

Build, maintain, and improve data pipelines, cloud data platforms, warehouses, databases, and integrations, ensuring data security, reliability, and quality. Prepare data for analytics and machine learning, maintain dashboards and reports, and develop a governed semantic layer that supports business intelligence and AI-enabled workflows.

Requirements

At least four years of experience is preferred, along with strong knowledge of ETL, data modeling and storage, backend data warehouse development, cloud services, and integrating multiple data sources. Candidates should be proficient in a programming language such as Python, C#, R, or Java, familiar with Redshift, Snowflake, Tableau, and Power BI, and able to communicate and solve problems effectively with technical and non-technical stakeholders.

Skills

Data Engineering
ETL
Data Modeling
Python
C#
R
Java
Relational Databases
NoSQL
Cloud Infrastructure
Distributed Computing
Amazon Redshift
Snowflake
Tableau
Power BI
MLOps

Visa sponsorship

Not detected in the job text

Keywords

Data Engineering
Data Pipelines
ETL
Data Acquisition
Data Integration
Data Cleansing
Data Enrichment
Data Warehousing
Data Modeling
Semantic Layer
Cloud-Native Data Platforms
Cloud Infrastructure
AWS
Azure
GCP
Amazon Redshift
Snowflake
Snowflake Cortex
Python
C#
R
Java
Relational Databases
NoSQL
Distributed Computing
MLOps
Machine Learning
Tableau
Power BI
AI-Assisted Development

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