Race RosterVerified Job Source

Data Platform Engineer

Canada
Remote
Posted Aug 12, 2026
1 position

Opens an external site

Employment type: Full-time
Experience level: Senior · 8+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week

Job summary

You will build and maintain a greenfield enterprise data platform using modern ELT ingestion and layered transformation pipelines. The role involves optimizing cloud platform costs and empowering business stakeholders with actionable, story-driven data insights.

Job details

Position Overview: We are building our enterprise data platform from the ground up, and we are looking for a Senior Data Platform Engineer to partner directly with our Data Architect to turn a greenfield vision into an enterprise-grade reality. In this foundational role, you won't be maintaining legacy technical debt, you will be laying down the very first lines of code and infrastructure for our data ecosystem. Crucially, you recognize that data engineering is not just about building pipelines; it’s about empowering actionable storytelling. You will bridge the gap between technical ingestion and business value - ensuring we collect, structure, and model the right data to power insights for Race Roster and our customers, while keeping our cloud platform cost-efficient and lean. Product Line: This role will be working on the Race Roster product. Work Location: The successful candidate for this role can be either a remote employee (working 100% remotely from a designated location within Canada), a hybrid employee (flexibility to work in the office or from home at a location within 75 km from the London, ON office), or an in-office employee at our London, ON office. Responsibilities: * Actionable story-driven engineering: Partner with the Data Architect and help build our greenfield medallion data architecture and data models that will empower internal users and customers with actionable insights around participant growth, revenue over time, registration dynamics, pricing tiers, and event performance. * FinOps & cost efficiency by design: Design compute and storage architectures in Snowflake with financial discipline from Day 1 - optimizing query performance, warehouse utilization, and storage patterns to maximize platform ROI and prevent runaway costs. * Modern ELT ingestion infrastructure: Build our first generation of ELT ingestion pipelines using Python frameworks like dlt (data load tool) to extract raw data from AWS RDS, third-party APIs, and webhooks into Snowflake, preserving raw data states to guarantee clean data lineage. * Layered transformation pipelines: Build our bronze → silver → gold transformation flow as small, focused, easy-to-understand Snowflake Dynamic Tables - exposing only the curated gold layer to our BI tool (Metabase) and downstream consumers. * Snowflake foundation & observability: Set up Snowflake from the ground up, implementing modern features like Snowpipe Streaming, Dynamic Tables, and Snowflake-native serverless orchestration (Tasks) - a deliberate choice over external schedulers. Bake quality into the platform using Snowflake Data Metric Functions (DMFs), native data lineage tracking, and Dynamic Data Masking. * AI/ML integration infrastructure: Build the foundational pipelines required to feed Race Roster’s AI initiatives - supporting custom model training workflows, feature stores, and cloud AI service integrations (e.g., Amazon Rekognition). * Fast local analysis & data apps: Leverage Python notebooks (Jupyter, marimo), DuckDB, and Parquet/Arrow for rapid local prototyping, paired with Streamlit or Gradio to build lightweight prototype data apps and dashboards that turn complex analysis into clear stories for stakeholders. * AI-accelerated engineering: Actively integrate AI coding assistants and LLM-powered SQL/Python generation into your daily workflow to accelerate platform buildout, write tests, and document systems as we build them. Education & Experience: * Product & value mindset: 5+ years of data engineering experience with a demonstrated focus on business outcomes over raw plumbing. Proven ability to ask "Why are we building this dataset and what actionable story does it tell?" before writing code. * Architectural cost discipline: Hands-on experience managing and optimizing cloud infrastructure costs (FinOps), particularly around Snowflake warehouse sizing, query profiling, and auto-suspend configuration. * Platform Ownership: Establish clean engineering standards around FinOps, data lineage, and data quality as foundational practices. * Modern ELT & data modeling: Understanding load-then-transform (ELT) architectures with Snowflake. Proven ability to translate complex source systems into clear architecture and data flow diagrams, then build clean dimensional or domain-driven data models ready for storytelling. * Expert SQL: Advanced SQL fluency - window functions, CTEs, semi-structured data (JSON/VARIANT), and reading query profiles to diagnose and optimize slow or expensive queries. * Snowflake expertise: Strong hands-on experience with Snowflake core concepts, streaming ingestion patterns, performance tuning, and native governance features (DMFs, lineage, dynamic masking). * Orchestration: Experience orchestrating and scheduling data pipelines - with Snowflake Tasks and Dynamic Tables refresh, or external tools like Airflow, Dagster, or Prefect - and sound judgment about when each approach fits. * BI tooling fluency: Understanding of how BI tools consume data models, with experience in Metabase or similar (Looker, Tableau, Power BI) and designing gold-layer models that make self-serve analytics easy. * Data privacy & governance: Experience handling PII responsibly - masking, role-based access controls, and retention - with working knowledge of privacy regulations such as GDPR and CCPA. * Ingestion & data formats: Experience ingesting data from relational databases (AWS RDS / Postgres / MySQL), AWS SQS, and REST APIs — ideally with declarative Python ingestion frameworks like dlt — alongside fluency with modern file and execution formats like Parquet, Apache Arrow, and DuckDB. * Python tooling ecosystem: Proficiency in Python, including interactive notebooks (Jupyter, marimo) and rapid frontend/dashboard frameworks (Streamlit, Gradio, etc.). * AI/ML readiness: Experience preparing structured and unstructured data pipelines for machine learning workflows or third-party computer vision / cloud AI APIs (e.g., Amazon Rekognition). * DevOps fundamentals: Familiarity with Infrastructure as Code (Terraform), Git workflows, CI/CD automation, and containerized environments (Docker). * Communication & comfort with ambiguity: Thrives in a small founding team where requirements evolve - able to work through ambiguity independently and communicate technical decisions and trade-offs clearly to non-technical stakeholders. * Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. * 8+ years of experience in software development The successful candidate for this role will become an employee of Race Roster North America Corporation (doing business as ASICS Apps Canada), a subsidiary of ASICS Corporation, a Japanese multinational corporation. Race Roster was founded in London, Ontario in 2011. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, sex, place of origin, ethnic origin, ancestry, citizenship, creed, record of offenses, genetics, disability, age, marital status, family status, veteran status, or fitness level. Accommodations are available on request for candidates taking part in all aspects of the selection process. Job applications will be reviewed by Rippling's AI. This posting is for an existing vacancy.

What you’ll do

Requirements

Candidates must have 5+ years of data engineering experience with strong expertise in Snowflake, SQL, and Python. A bachelor's or master's degree in a technical field or equivalent practical experience is required.