Analytics Engineer

FORM•Vancouver, BC

11d

About The Position

We’re seeking an experienced Analytics Engineer to strengthen FORM’s analytics data foundation and business intelligence capabilities. Working primarily in SQL, dbt, Amazon Redshift and Tableau, you will transform raw product, subscription, ecommerce and financial data into trusted, reusable models and translate those models into clear, decision-ready dashboards and visualizations. This is a hands-on role for someone who can contribute quickly, take ownership of production work and leave well-tested, documented systems and intuitive reporting behind.

Requirements

3 - 5 years of experience in analytics engineering, data engineering or a highly technical analytics role.
Advanced SQL skills and strong knowledge of dimensional data modelling.
Hands-on experience building, testing and documenting production data models with dbt.
Experience working with a cloud data warehouse; Amazon Redshift and AWS experience are preferred.
Experience using Git, code review and software engineering practices in an analytics environment.
Working proficiency in Python for data pipelines, integrations or automation.
Experience with Tableau or another modern business intelligence platform.
Ability to troubleshoot data issues across source systems, transformation pipelines and downstream reporting.
Strong communication skills and the ability to turn ambiguous business questions into clear, maintainable data solutions.

Nice To Haves

Experience with Dagster, Airflow or another workflow orchestration platform.
Experience with Fivetran or other managed data ingestion tools.
Experience with subscription, ecommerce, product telemetry or financial data.
Experience working in a fast-paced startup or consumer technology environment.

Responsibilities

Build and maintain scalable SQL and dbt models in Amazon Redshift.
Transform raw product, subscription, ecommerce, marketing and financial data into clean, reusable analytics datasets.
Define and maintain consistent business metrics, including revenue, MRR, churn, retention, trial conversion, customer lifetime value and sales performance.
Implement automated data-quality tests, documentation and lineage so stakeholders can trust and understand the data.
Monitor scheduled analytics workflows, investigate failures and improve pipeline reliability.
Develop and maintain Tableau data sources and dashboards using curated warehouse models.
Build Python-based data integrations and automation where SQL and dbt are not the right tools.
Improve Git-based development, code review, deployment and CI/CD practices for analytics.
Partner with Analytics, Data Engineering and business stakeholders to translate requirements into durable data products.