Data & AI Engineer

About the Role

Secretlab is a global gaming chair brand trusted by over three million users, with primary markets including the US, Europe, and its Singapore headquarters. This position represents a progressive evolution from analytics engineering into an integrated role combining data and AI engineering. Your responsibility will be to design precise data models that accommodate machine reasoning, as agents now actively consume data analytics work.

This position involves simultaneous responsibilities in data engineering and AI engineering, which enhance each other from day one. You will deliver impactful work immediately — creating real data models, automation feedback loops, and serving actual users. Every piece of code undergoes formal reviews at all levels, supported by documented growth trajectories with transparent expectations and measurable reviews.

Role Responsibilities

• Develop accurate, tested data models with thoughtfully staged layers and appropriate granularity ensuring reviewers can verify your logic with ease.
• Create automation loops to eliminate monotonous tasks, starting with your own, including mechanisms that validate daily operational success.
• Embrace AI-assisted development workflows where AI generates initial drafts and you deliver the final output, aligned with department standards.
• Advance rapidly by proactively seeking clarifications, flagging complexities early, and avoiding repeated correction feedback.
• Engage regularly in data engineering activities including building and maintaining dbt models on Snowflake, and participate in bidirectional code reviews.
• Build automation loops that enhance team efficiency and incorporate validations confirming successful runs.
• Monitor and maintain data pipeline operations, addressing ingestion problems and data quality alerts to uphold data reliability.
• Coordinate weekly with AI Engineering leadership to discuss your projects and lessons learned.

Essential Qualifications

• Between 2 and 5 years of professional experience in data, analytics, or software roles where the deliverables were actively utilized; notable side projects are a plus, but not a substitute for experience.
• Proficient SQL skills, including joins and window functions, with the ability to articulate reasons behind query performance issues; practical testing included.
• Solid understanding of data modeling principles such as the purpose of data layers and the implications of grain definition and inappropriate joins.
• Competent Python scripting skills and disciplined version control practices using git.
• Demonstrated experience applying AI automation to improve real workflows beyond theoretical coursework, with clear explanations of design choices.
• Capability to provide evidence-based assurance of your work’s correctness using verifiable methods rather than subjective confidence.

Preferred Qualifications

• Experience with dbt for data modeling and transformations.
• Familiarity with Snowflake or other cloud-based data warehouses.
• Knowledge of orchestration tools like Apache Airflow.
• Experience implementing operational checks such as test sets or assertions for automation validation.
• Exposure to AWS, Terraform, or DevOps practices is advantageous but not mandatory.
• Proven delivery of solutions used by others in a production environment.

Growth Opportunities

• Learn cloud infrastructure and orchestration operations, particularly AWS and Terraform, under supervision.
• Develop skills operating production AI agents, handling incidents, telemetry, and alerting systems.
• Master evaluation disciplines such as test sets, automated grading, and regression testing, building on hiring instincts.
• Advance along dual career tracks: deepen AI engineering expertise or enhance data engineering proficiency, with clear written expectations guiding advancement.

Preferred Traits

• Adheres to department-wide principles: operate with AI as a native tool, take ownership of deliverables, focus on solving business challenges, communicate candidly, and improve processes continuously.
• Seeks balanced engagement in both data and AI engineering disciplines rather than exclusively one side.
• Open and honest about abilities, receptive to constructive feedback in either direction.
• Pragmatic approach, prioritizing understanding the purpose behind tasks and avoiding over-engineering.
• Proactively identifies and owns errors early, preventing recurrence.

Role Clarifications

• This position is unsuitable for those who only experiment with prompts without solid engineering and SQL capabilities.
• Candidates interested in exclusively one facet (data or AI) are not appropriate for this dual-focused role.
• This is not a BI/dashboard-only role; it centers on engineering data infrastructure underpinning dashboard systems.