Analytics Manager

Role Overview

This position exists to ensure a dependable data platform and an internal AI layer enabling domain teams to obtain reliable answers from their data. As an analytics engineer lead, you will oversee the release process, maintain seamless integration between the data warehouse and AI tools, and develop an in-house AI agent build on this robust foundation.

Key Responsibilities

• Manage a trustworthy data platform by establishing and upholding release procedures with branch protections, CI standards, code reviews, and orchestration to allow safe, predictable deployment of warehouse changes.
• Maintain secure, cost-efficient connections between the data warehouse and AI layers through access control, cost tracking, and security setups.
• Implement change management practices to ensure upstream modifications do not unexpectedly disrupt downstream AI tools.
• Enforce consistent quality in data models by requiring thorough documentation, test coverage, and pull request approval to build analyst confidence in data trustworthiness.
• Ensure pipeline errors are detected before production rollout, enabling automatic system alerts rather than stakeholder complaints about failures.
• Develop and maintain a reliable AI knowledge platform, integrating data and documentation ingestion pipelines collaboratively with engineers and analysts.
• Automate the flow of business context into AI workspaces, eliminating manual efforts for analysts.
• Build an internal text-to-SQL agent with engineering partners following documented, extensible architecture for user-friendly querying.
• Collaborate with domain analysts to validate AI output accuracy prior to deployment to ensure trustworthy workspace responses.
• Elevate automated reporting from prototype to production quality with built-in freshness verification, error detection, and failure handling.

Success Milestones

• Within 3 months: Establish release and change management practices to secure warehouse updates and protect AI tools from upstream disruptions.
• By 6 months: Connect domain workspaces to the AI platform and deploy the in-house text-to-SQL agent in production.
• By 12 months: Operate automated reporting with freshness and failure checks, stabilize platform operations, and empower domain teams to self-serve analytics.

Qualifications and Experience

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or a similar discipline.
• Minimum of 5 years’ experience in analytics or data engineering, demonstrating successful production system deliveries.
• Advanced Python skills for production scripting, pipeline creation, and API integration.
• Proficiency with modern data transformation frameworks such as dbt or equivalents, including modelling, documentation practices, testing, and CI/CD deployment.
• Strong SQL expertise along with hands-on experience in BigQuery or similar cloud data warehouses handling complex transformations and performance optimization.
• Practical knowledge of workflow orchestration tools like Airflow or equivalents in production environments.
• Experience using large language model APIs (OpenAI, Anthropic, etc.) in at least one project and enthusiasm to integrate this technology into core responsibilities.
• Robust software engineering skills encompassing version control, CI/CD pipeline design, API design, automated testing, and code review practices.
• Proven capability to work collaboratively across diverse technical teams and influence without formal authority.
• Strong communication skills to effectively engage in varying professional settings with technical and non-technical stakeholders.
• Excellent interpersonal abilities to build and sustain stakeholder relationships.
• Legal eligibility to work in Singapore.

Preferred Skills

• Familiarity with agent orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar technologies.
• Experience using major cloud platforms (Google Cloud Platform, AWS, or Azure) and managed data services.
• Knowledge of data cataloging or metadata management tools like OpenMetadata, DataHub, or Alation.
• Exposure to enterprise deployments of LLM platforms and access control management.
• Practical understanding of Retrieval-Augmented Generation (RAG) or document retrieval systems.
• Interest or experience in investment, financial services, or multi-asset trading industries.

Benefits and Working Environment

• Health and wellness provisions including comprehensive medical insurance, meal allowances, team-building events, and mobile fitness benefits.
• Competitive salary structures with yearly performance-linked bonuses and support for relocation.
• Clear paths for career growth with opportunities for cross-functional skill development.
• Flexible working arrangements with hybrid options and comprehensive paid leave policies to support sustainable work-life balance.

Culture and Growth

At Pluang, growth occurs creatively and dynamically through real ownership and broad scope. Employees progress into roles influencing significant decisions and collaborating across global domains. Success is measured by impact rather than volume of work.

Candidate Profile

The ideal candidate demonstrates a founding mindset, courage to act decisively, dedication to work integrity, and thrives in an environment combining peer support with intellectual challenge. Those ready to build the future of finance through meaningful, compounded effort will find this role rewarding.