About the job

ABOUT THE COMPANYPunt 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 OVERVIEWWe 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.

RESPONSIBILITIESPipeline & 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 BRINGExperience: 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 HAVEExposure 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.

Responsibilities

About the job

RESPONSIBILITIESPipeline & 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 BRINGExperience: 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 HAVEExposure 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.