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Punt

Associate Data Engineer

Vancouver, BC, Canada

$80,000 to $100,000 CAD / year · Full-time

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Job details

Pay: $80,000 to $100,000 CAD / year
Job type: Full-time
Apply by: Nov 28, 2026
Experience: Experience: 2 years of professional experience in data engineering, analytics engineering, or backend engineering (internships and co-ops included).
Posting language: English
Posted: Oct 29, 2026

Original job description

ABOUT THE COMPANY

Punt is a Vancouver-based, founder-led technology company behind two of the largest social casinos in North America. We’re evolving into a prediction-led super app - integrating prediction markets, social gaming, and new product surfaces into a single, cohesive experience. Our backend already operates at scale, processing high-volume, real-money transactions. We’re building toward a new category: the most advanced and engaging social gaming platform in the world.

ROLE OVERVIEW

We are seeking an Associate Data Engineer to design, build, and maintain the core data pipelines and analytics models that power our platform. Working alongside senior engineers, you will support our cloud data infrastructure by ingesting high-volume transactional datasets, writing production transformations, and ensuring data accuracy for business-critical applications.

We embrace modern engineering workflows, including AI-assisted development (e.g., Copilot, Cursor, LLMs). We are looking for early-career candidates who actively leverage AI tools to accelerate output, automate routine tasks, and learn faster - while maintaining the critical thinking required to review, debug, and take full ownership of the code they deploy.

Above all, we value strong SQL fundamentals, working Python proficiency, and the instinct to double-check your output for correctness before someone else has to.

RESPONSIBILITIES

Pipeline & Model Development: Build and maintain dbt models, write clean transformations, establish data testing suites, and adhere to established modeling conventions using modern, AI-assisted development tools.
Data Ingestion & Integration: Develop and operate robust extract/load (ELT) pipelines in Python to ingest data from relational databases, APIs, and event streams.
Platform Evolution & Support: Assist in maintaining and upgrading cloud data platform infrastructure, including data reconciliation, schema migrations, and metric validation across storage layers.
Data Quality & Monitoring: Implement freshness, volume, and anomaly checks to catch edge cases and prevent stale or corrupted data from reaching stakeholders.
Incident Response & Debugging: Investigate pipeline failures, analyze error logs, identify root causes, and ship fixes or provide detailed handoffs.
Cross-Functional Collaboration: Partner with analysts and product teams to translate ad-hoc data requests into scalable, reusable modeled datasets.
Modern Engineering Practices: Actively incorporate modern AI coding assistants into your daily workflow to write boilerplate code, draft documentation, and generate unit tests efficiently.

WHAT YOU BRING

Experience: 2 years of professional experience in data engineering, analytics engineering, or backend engineering (internships and co-ops included).
AI-Forward Mindset: Openness and enthusiasm for using AI development tools (e.g., GitHub Copilot, Claude, Cursor) to speed up execution, learn new technologies, and improve code quality.
SQL Proficiency: Strong knowledge of joins, aggregations, window functions, and query optimization logic.
Python Skills: Practical experience writing Python scripts for data manipulation, API integration, and automated task execution with proper error handling.
Data Modeling Fundamentals: Core understanding of analytical data modeling (dimensional modeling, grain, incremental loading vs. full reloads).
Engineering Best Practices: Familiarity with Git workflows (branches, pull requests, code reviews) and clear technical documentation.
Data Quality Instincts: A habits-first approach to validating query outputs and pipeline results prior to deployment.
Bachelor's degree in Computer Science, Engineering, Statistics, or a related discipline, or equivalent practical experience.

NICE TO HAVE

Exposure to dbt or another transformation framework.
Exposure to a cloud data warehouse or lakehouse - Databricks, Snowflake, Redshift, or BigQuery.
Exposure to a workflow orchestrator (Airflow, Prefect, Dagster, Databricks Workflows) or to scheduled jobs in any form.
Exposure to AWS or another cloud platform.
A personal, academic, or internship project where you built a pipeline end to end and can explain the choices you made.
Interest in gaming, fintech, payments, or another high-volume transactional domain.

WHY JOIN PUNT?

Help scale a fast-growing social gaming platform with real technical and product impact.
Work on high-throughput backend systems where performance, reliability, security, and operational excellence matter.
Join a collaborative engineering team that values strong technical judgment, ownership, and pragmatic architecture.
Use modern engineering practices, including AI-assisted development, automation, observability, and cloud-native infrastructure.
Influence backend architecture, platform standards, and the technical direction of a growing engineering organization.
Work in a high-growth environment with opportunities for learning, ownership, and leadership.
Competitive compensation and a comprehensive benefits package.

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More about this job

Responsibilities

Build and maintain dbt models, Python ELT pipelines, and data quality monitoring for high-volume transactional data. Support cloud data infrastructure, troubleshoot pipeline incidents, and collaborate with analysts and product teams to deliver reusable datasets.

Requirements

Requires two years of professional experience in data, analytics, or backend engineering, including internships or co-ops, along with strong SQL and practical Python skills. Candidates should understand analytical data modeling, Git workflows, and data validation, and have a bachelor's degree in a related field or equivalent practical experience.

Skills

SQL
Python
Data Pipeline Development
Dbt
Data Modeling
Data Quality Testing
ELT
API Integration
Git
Cloud Data Platforms
Workflow Orchestration
Debugging
Query Optimization
AI-Assisted Development

Benefits

Competitive Compensation

Visa sponsorship

Not detected in the job text

Keywords

SQL
Python
Dbt
GitHub Copilot
Claude
Cursor
LLMs
ELT
Data Pipelines
Data Modeling
Dimensional Modeling
Incremental Loading
Data Quality
API Integration
Event Streams
Cloud Data Warehouse
Databricks
Snowflake
Amazon Redshift
BigQuery
Airflow
Prefect
Dagster
AWS
Schema Migrations
Query Optimization
Social Gaming
Prediction Markets
Real-Money Transactions

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