Senior Data Engineer (PST Overlap) | Remote

Appliscale is an independent Technology Consultancy specializing in bespoke, cloud-based systems and full-cycle engineering - from business analysis to architecture design, development, and deployment. They work across industries such as gaming, proptech, legaltech, and adtech, partnering with startups, scaleups, and Fortune 500 companies worldwide. Their current focus is on building intelligent agents that drive automation, streamline operations, and enable scalable agentic ecosystems. The company has a flat structure, remote-first culture, and self-organized teams.

• Minimum of 5 years commercial work experience in data engineering or a related field
• Bachelor’s or higher degree in Computer Science, Software Engineering, or a related field
• Proficiency in Python, essential for data processing and analysis tasks
• Hands-on experience with DBT (Data Build Tool) and Airflow
• Commercial experience using PySpark and Databricks
• Proficiency in AWS cloud infrastructure
• Effective communication and teamwork skills

Nice to have:

• Experience with ML Operations and GenAI pipelines and infrastructure
• Experience in the gaming industry, particularly with online multiplayer games
• Experience working with cross-discipline organizations that build data products
• Proficient in large-scale data manipulation across various data types
• Demonstrated ability to troubleshoot and optimize complex ETL pipelines
• Proven experience mentoring and guiding other engineers

• Please note, availability to attend afternoon/evening meetings is a requirement for this role as most of the team is located on the US West Coast (LA and Seattle)
• Design, build, and optimize scalable pipelines for data, supporting insight into the Data Foundation ecosystem
• Design and implement data models using industry best practices that capture a complete ecosystem view of internal customer experiences, while ensuring accuracy, scalability, and long-term usability
• Architect and implement robust, maintainable, and high-performance data solutions
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and pubsports areas of coverage
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility
• Evaluate and adopt new tools, frameworks, and methodologies to advance data engineering capabilities
• Support cost optimization by ensuring scalable and efficient data solutions
• Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA)
• Contribute to engineering discipline by shaping infrastructure, craft standards, tooling, and organizational best practices