Data & AI Engineer

About the Role

Secretlab, a global gaming chair company with more than three million users and primary markets in the US, Europe, and Singapore, is seeking a Data & AI Engineer. This position represents an advancement beyond traditional analytics engineering, blending data modeling carefully designed for machine reasoning and AI agent consumption. The role integrates data engineering and AI engineering practices from day one, emphasizing shipping effective models, loops, and user-facing solutions while maintaining strong code review and clear growth paths.

Key Responsibilities

• Develop accurate, layered data models that are logically sound and rigorously tested, ensuring peer reviews affirm your logic without re-derivation.
• Create automation loops to relieve repetitive tasks, starting with your own, always providing verifiable proof that they function correctly.
• Adopt an AI-native mindset by utilizing AI tools to draft solutions, while retaining ownership and responsibility for the final outcomes.
• Continuously improve by seeking early feedback, escalating complex issues promptly, and avoiding repeated review comments.
• Engage in data engineering tasks such as building and maintaining dbt models on Snowflake and participating actively in code reviews.
• Automate operational workflows and monitor data ingestion and quality flags to maintain trustworthy data pipelines.
• Communicate regularly with the AI Engineering Lead to align on development progress and personal learning goals.

Experience and Skills Required

• 2 to 5 years of relevant professional experience in data, analytics, or software development roles where your outputs were relied upon.
• Proficiency in SQL, including joins and window functions, with the ability to troubleshoot performance issues on the spot.
• Solid understanding of data modeling principles, including data layers, granularity, and the consequences of improper join strategies.
• Competent Python scripting skills paired with disciplined git version control practices.
• Practical experience in integrating AI automation into your workflows, beyond theoretical knowledge, with demonstrable projects or implementations.
• Ability to validate the accuracy of your work through documented evidence and reliable methods rather than assumptions.

Preferred Qualifications

• Experience with dbt for data modeling and transformation.
• Familiarity with Snowflake or other cloud data warehouses.
• Experience using orchestration tools such as Airflow.
• Developed automation or agents with embedded error checking mechanisms like test sets or assertions.
• Exposure to AWS, Terraform, or DevOps practices, though not required at this level.
• History of delivering products or features used by others beyond yourself.

Professional Growth Opportunities

• On-the-job learning of cloud infrastructure and orchestration using AWS and Terraform under supervision.
• Advancement to operating production AI agents, including incident management and monitoring.
• Mastering evaluation disciplines such as automated grading and regression testing.
• Potential career paths include progressing fully into AI engineering or deepening expertise as a data engineer with AI foundations.

Work Environment and Team Expectations

• All team members uphold five core expectations: embracing AI-native workflows, owning deliverables, focusing on business problems, communicating openly, and improving processes.
• The role requires a balance of enthusiasm for both data engineering and AI disciplines.
• Honesty about skills and openness to feedback in all directions are essential.
• Pragmatic approach to requests, avoiding over-engineering.
• Responsibility for identifying and correcting mistakes early to prevent recurrence.

Role Clarifications

• This position is unsuitable for candidates focused solely on prompt engineering without a solid foundation in data engineering.
• Candidates who prefer single-focus roles and dislike multitasking between AI and data aspects should not apply.
• This is not a dashboard or BI-only position; the focus is on building the foundational data models underlying analytics.