Vancouver, BC · Open to relocation across Canada

Tanye (Xin) Tan snowboarding

Tanye (Xin) Tan

Data Engineer / Architect · AI/ML Developer · Consultant · Builder · Yapper — currently in Sports & Entertainment

01

About

I'm a data engineer based in Vancouver, currently working in the sports and entertainment industry. My work sits across data platforms, pipeline development, and analytics infrastructure, with a particular interest in building reliable systems that turn complex data into something useful. Working at a consultancy has also pulled me toward the leadership and client-facing side of the job — translating complex technical work for stakeholders, and mentoring teammates along the way. I like that part just as much as the coding.

More recently, I've been spending time exploring AI and machine learning — particularly where they intersect with data engineering, from AI-assisted development to the infrastructure and data foundations behind intelligent systems.

I hold a Master's degree in Computer Science from Simon Fraser University. Fun fact: I've worked at both Apple and Samsung. I've also worked across a few different engineering environments, which has shaped how I think about data, systems, and problem-solving.

Originally from Guangzhou and now exploring Canada, I try to stay away from the keyboard when I can — tennis, snowboarding, running, or finding another reason to get outside.

Languages
Python SQL Scala R Java C
Data & cloud
AWS GCP dbt Airflow Spark Hadoop Databricks Snowflake Docker Kubernetes Git
02

Experience

Data Engineer · Two Circles

May 2025 — Present

Vancouver, Canada · sports & entertainment data consultancy

  • Led design and delivery of a real-time ingestion pipeline (AWS Kinesis, Lambda, SNS, DynamoDB) processing 80K+ events/day — shipped in 3 months and now powering 4+ client use cases.
  • Own 50+ dbt models across subscription, transaction, and clickstream domains, orchestrated by daily Airflow pipelines; led a Redshift migration and large historical backfills.
  • Technical lead on a client-facing sports-industry engagement — translating business needs into technical solutions across engineering, product, and analytics, and mentoring newer teammates.
  • Won the company hackathon building an AI-powered sports-challenge app, now scoped as an internal tool.

Data Engineer · Samsung R&D Canada

May 2024 — May 2025

Vancouver, Canada · co-op, then contract

  • Built and maintained ETL pipelines in Airflow (Python, SQL, PySpark) across GCP BigQuery, AWS Redshift, EC2, Athena, and S3.
  • Automated data ingestion via APIs, CI/CD with GitHub Actions, and inactive-user management on Tableau Server DB; delivered KPIs supporting both B2B and B2C analytics.

iPhone MDE Intern · Apple Inc.

Apr 2023 — Aug 2023

Suzhou, China

  • Applied deep learning to optimize iPhone 15 manufacturing, from data collection to model training and on-device deployment.
  • Built a Tableau dashboard for real-time monitoring of production-capacity metrics.
  • Maintained the team's internal site and worked directly with manufacturing vendors across several factories.

Data Intern · SHUWEI

Dec 2022 — Feb 2023

Shenzhen, China

  • Used SQL/Python to process urban datasets — transportation, GDP, consumer spend — for retail site-selection analysis.
  • Ran statistical analysis on the DataArts Studio cloud platform (Hive/Presto).

Research Assistant · Dalhousie University

Jul 2022 — Oct 2022

Halifax, Canada · Mitacs Globalink Research Internship · Data Analytics for Clean Water Technologies

  • Studied the effect of water-quality indexes on wastewater plant performance in R.
  • Built an R Shiny + HTML dashboard for water-treatment-focused projects.

Winter Research Program · North Carolina State University

Jan 2022 — Feb 2022

Raleigh, USA · Netflix Prize Movie Ratings Prediction

  • Used Model-based Collaborative Filtering as a baseline and explored GNN-based methods to improve movie-rating predictions on the Netflix dataset.

Research Assistant · Jilin University

Jan 2022 — Feb 2022

Changchun, China · Intelligence-control software for sandstone-type uranium mining

  • Used well-logging data from a mining company to develop an LSTM model for lithology identification and ore-grade prediction in deep formations.
03

Education

SFU logo

Simon Fraser University · Sep 2023 – May 2025

MSc, Professional Computer Science (Big Data track) · includes an 8-month co-op · Mitacs Globalink Graduate Fellowship ($15,000)

Courses: Big Data Lab, Machine Learning, Distributed & Cloud Systems, Multimedia Systems, Databases

Jilin University logo

Jilin University · Sep 2019 – Jul 2023

B.Sc. Computer Science and Technology (2021–2023) & B.Eng. Environmental Engineering (2019–2021)

Courses: C, Java, Data Structures, Web Design, Java EE Architecture, Python for Data Analysis

University of Oxford logo

University of Oxford · Jan 2020 – Feb 2020

Exchange Student, Oxford Prospect Programme, Regent's Park College · Best Presentation Award (4/30)

Guangzhou No.2 High School logo

Guangzhou No.2 High School · Sep 2016 – Jul 2019

High School Diploma, Science

Patent
Groundwater-level measurement device

A Semi-automatic Digital Measurement Device for Groundwater Level

Ying Lu, Yang Xu, Xin Tan, Chengyan Wen, Zhiyu Jiao

China Patent ZL 2021 1 0875594.0 · Filed Jul 2021 — Issued Sep 2022

04

Recognition

Honors & awards
Social activities
Miscellaneous
Sports Tandem skydiving (13,500 ft, Montreal) · tennis · snowboarding · skateboarding · surfing · hiking
Been to Canada · United States · Singapore · United Kingdom · Hong Kong · Macau
Try this Pearls Before Swine — a small NIM game worth a coffee break
05

Projects

CMPT984 — Deequ vs. DuckDB data quality benchmark

Main Developer · Coursework · Sep 2023 – Dec 2023

Built a data pipeline with Spark, Databricks, and GCP to benchmark Deequ against DuckDB for data-quality checks on streaming and batch data, as part of a graduate databases course.

Spark Databricks GCP
06

Contact

Open to conversations about data engineering, sports analytics, or just a chat about tennis and mountains. Reach out any time.