Silver gold transformation flow as small, focused, easy-to-understand Snowflake Energetic 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), Duck DB, 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 (Fin Ops), particularly around Snowflake warehouse sizing, query profiling, and auto-suspend configuration.Platform Ownership: Establish clean engineering standards around Fin Ops, 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 / My SQL), 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 Duck DB.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 ).Dev Ops 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 developmentThe 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.#J-18808-Ljbffr

📌 Data Platform Engineer (London)
🏢 Rippling
📍 London