[Wattpad] Data Engineer - Maternity Cover

WEBTOON Entertainment Inc. (Wattpad & WEBTOON Family of Brands)•Toronto, ON

1d•CA$100,000 - CA$140,000•Hybrid

About The Position

Wattpad is seeking a Data Engineer who is passionate about diving deep into data infrastructure, creating and enhancing data pipelines, and thrives in a product-focused environment. This role is an important part of the Data Engineering team, which empowers engineers, analysts, and data scientists with tools for data-driven decision making, analysis, and feature development. This is a 12-month fixed-term contract to backfill a maternity leave.

Requirements

4+ years of data engineering experience
Ability to write high-quality code in Python and PySpark
Strong understanding of database fundamentals and software engineering principles
Strong understanding of Data Contracts, microservices principles, and treating datasets/platforms as API-driven products
Deep understanding of two or more of these technologies: Spark, Airflow, AWS EMR, AWS Glue, Sagemaker and Redshift
Experience with batch processing and streaming systems
Exposure and/or strong interest in Kafka or other queuing technologies
A critical thinker and possess creative problem solving skills
Quantitative skills, ability to think analytically, and strong attention to details
A desire to contribute to the broader development community

Responsibilities

Coordinate with data scientists, data analysts, software engineers and product managers to design and evolve our existing architecture, improving the reliability and performance of our data infrastructure
Build, maintain and scale our ETL pipelines to help power data science, data products and analytical needs
Design, build, and maintain data infrastructure systems such as but not limited to AWS DMS, HBase, EMR, Redshift, S3, Kafka, Airflow while ensuring scalability, reliability, and security
Write new tools, services, and platforms in Python
Research new technologies to solve tomorrow’s data infrastructure needs
Advocate for the adoption of tools in the data engineering team