Middle+/Senior Data Engineer

This is for E2E Project, which is collecting player level data for our brands, which helps us to track revenue on various pages/brands and better understand how our users are using our products and what's trending. Our vision for this is to become the central source of truth for user journey insights, empowering our company to make smarter, faster, and more impactful decisions that drive commercial growth and product innovation.

Role requirements:

• 3+ years of experience as a Data Engineer or Software Engineer working on data infrastructure.
• Strong Python skills and hands-on experience with SQL and Snowflake.
• Experience with modern orchestration tools like Airflow and data streaming platforms like Kafka.
• Understanding of data modeling, governance, and performance tuning in warehouse environments.
• Ability to work independently and prioritize across multiple stakeholders and systems.
• Comfort operating in a cloud-native environment (e.g., AWS, Terraform, Docker).
• Python side:
  • must have is experience in pulling and managing data from APIs
  • nice to have would be web scraping via browser automation (playwright / selenium / puppeteer for example)

Role Responsibilities:

• Build connectors to external partners to harvest the data
• Build custom functions to process the data
• Integrate data into Snowflake and other reporting tools
• Work cross teams and cross functions to provide good quality and speed of data

• Champion and challenge existing solution to improve and optimize them even further

   

Key Skills/What they'll be working on:

• Design, build, and maintain ETL/ELT pipelines and batch/streaming workflows.
• Integrate data from external APIs and internal systems into Snowflake and downstream tools.
• Own critical parts of our Airflow-based orchestration layer and Kafka-based event streams.
• Ensure data quality, reliability, and observability across our pipelines and platforms.
• Build shared data tools and frameworks to support analytics and reporting use cases.

• Partner closely with analysts, product managers, and other engineers to support data-driven decisions.