Analytics Engineering Lead

Role Purpose

This position is critical to ensuring that our data platform remains robust and trustworthy, empowering business decisions and underpinning AI functionalities. The role focuses on maintaining a stable data warehouse environment that does not disrupt downstream tools, and developing an internal AI layer that enables domain teams to obtain dependable insights directly from their data. This role entails ownership of the entire release process, the link between the warehouse and AI systems, and upkeep of data pipelines to keep AI workspaces current. Collaboration with domain analysts is key to shaping what the AI understands and validating answer accuracy.

Responsibilities

• Develop and oversee a reliable data platform foundation for the business.
• Implement and manage a controlled release process for warehouse changes, incorporating branch protection, continuous integration standards, code reviews, and reliable orchestration that does not depend on individual reviewers.
• Configure and maintain stringent access controls, cost monitoring, and security settings for the enterprise AI platform's warehouse interface.
• Establish a change management routine alongside data engineering to ensure any upstream modifications are assessed before rollout, preventing unforeseen impacts on AI tools.
• Maintain consistent quality standards for data models through documentation mandates, test coverage, and pull request requirements to guarantee trustworthiness.
• Ensure pipeline validation mechanisms catch failures prior to production deployment, alerting teams proactively rather than reactively through stakeholder complaints.
• Partner with engineers and analysts to build data and document ingestion pipelines supporting a trusted AI knowledge platform.
• Automate business context integration into AI workspaces, eliminating manual efforts for analysts through steady, repeatable ingestion processes.
• Design and launch an in-house text-to-SQL agent with a well-documented, extendable architecture, backed by engineering collaboration.
• Implement accuracy verification protocols signed off by domain analysts before AI outputs become operational.
• Advance reporting tools from prototype phases to production-ready status, ensuring data freshness validation, failure awareness, and robust error handling.

Success Milestones

• Within first 90 days: Established safe release and change management processes that protect downstream AI systems.
• By six months: Domain workspaces connected to the AI platform and operational, with the text-to-SQL agent deployed in a production environment.
• Within one year: Automated reporting fully operational with checks for timeliness and errors, a stable platform foundation, and domain teams independently answering queries without analyst mediation.

Qualifications and Experience

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or related disciplines.
• More than five years of professional experience in analytics or data engineering with proven production delivery across data-focused systems.
• Advanced skills in Python for production scripting, pipeline construction, and API integrations.
• Extensive experience using dbt or a comparable modern data transformation framework, including data modeling, documentation, testing, and CI/CD processes.
• Strong proficiency in SQL and practical experience with BigQuery or an equivalent cloud data warehouse, focusing on complex transformations and performance optimization in queries.
• Production experience using orchestration platforms such as Airflow or its alternatives.
• Hands-on involvement with large language model APIs like OpenAI or Anthropic in projects or personal settings, complemented by eagerness to make this a core responsibility.
• Robust software engineering background encompassing version control, CI/CD pipeline architecture, API design, comprehensive testing, and systematic code reviews.
• Proven collaboration skills across culturally diverse technical teams, capable of influencing outcomes without formal authority.
• Excellent communication abilities bridging technical and non-technical stakeholders in various professional contexts.
• Strong interpersonal skills with a history of successful stakeholder relationship management.
• Eligibility to work legally in Singapore.

Desirable Skills

• Experience with agent orchestration frameworks such as LangChain, LangGraph, or CrewAI.
• Familiarity with major cloud platforms (GCP, AWS, Azure) and their managed data services.
• Knowledge of metadata management or data catalog tools like OpenMetadata, DataHub, or Alation.
• Exposure to enterprise deployment of large language model platforms and access control systems.
• Understanding of Retrieval-Augmented Generation (RAG) or document retrieval systems.
• Interest in the financial sector, specifically investment, financial services, or multi-asset trading.

Benefits and Culture

• Comprehensive medical insurance and wellness benefits including meal stipends, team-building events, and mobile fitness programs.
• Competitive salary packages supplemented by performance-based annual bonuses and relocation assistance.
• Clear pathways for career progression and flexibility to shift expertise across different lines of business and roles.
• Hybrid working arrangements and generous paid leave to support sustainable work-life balance.

Company Philosophy

At Pluang, the growth opportunity is vast and dynamic due to meaningful ownership, broad scope, and a culture that challenges conventional thinking. Employees are empowered to drive impactful decisions, collaborate globally, and track success by tangible change rather than volume of tasks. The pathway is seldom linear but always purposeful.

Candidate Profile

The ideal candidate demonstrates a founder’s mindset, is courageous in execution, and prides themselves on high-quality work integrity. Thriving in an environment balanced between peer support and intellectual challenge is essential. Commitment to making lasting contributions within a values-driven organization is paramount.