Data Analytics Manager (Analytics Engineering)
Analytics Engineering | Singapore
As Analytics Engineering Lead, you own the engineering foundation that keeps our data platform stable and our AI tools trustworthy. That means the release process, CI standards, orchestration health, data warehouse security, and the change management practices that prevent downstream breaks.
On top of that foundation, you lead the AI layer by connecting business domains to our AI knowledge platform, designing and evolving in-house agentic systems that replace our current SaaS tools, and governing AI infrastructure across multiple engineering teams. The role sits at the intersection of data platform engineering and applied AI. You own both.
What You Will Be Doing
Data Platform Engineering
- Own the end-to-end release process, including branch protection, CI standards, code review practices, and orchestration health, ensuring the process is resilient and does not depend on any single reviewer
- Manage and secure the data warehouse connection to our enterprise AI platform, including access controls, cost monitoring, and security configuration
- Establish and maintain change management practices with data engineering, so that upstream data model changes are communicated and impact-assessed before they reach downstream AI tools
- Define and enforce data model CI/CD standards, including documentation requirements, test coverage, and PR gates
- Own orchestration reliability by building validation practices that surface pipeline failures before they reach production
AI Platform & Agent Systems
- Own the AI knowledge platform infrastructure, including data warehouse connectivity, document ingestion pipelines, and LLM configuration across business domain workspaces
- Design and maintain document ingestion pipelines that keep business context and institutional knowledge flowing into AI workspaces on a consistent cadence, without manual steps from analysts
- Design and build in-house agentic systems, starting with a text-to-SQL agent, and expand the architecture as use cases grow across the organisation
- Own the AI accuracy and evaluation framework, covering test case design, scoring, and improvement cycles that gate production deployment
- Own the data catalog strategy and implementation, including data model ingestion, column-level lineage, and business glossary, so data is discoverable and accessible to both analysts and AI tools
- Partner with domain analysts to bring automated reporting from proof-of-concept to production quality, with appropriate data freshness checks, failure detection, and error handling
- Design and evolve workflow agents in partnership with domain analysts, defining agent architecture, human-in-the-loop review layers, and the accuracy standards required before reducing manual oversight
- Maintain the AI governance model across data engineering, quantitative research, platform, and analytics teams, with agreed prioritisation and decision-making to avoid duplicated effort
- Maintain market and external data feeds in the warehouse with defined freshness standards for use across AI workspaces
What You Need to Be Successful
Required
- Bachelor's or Master's degree in Computer Science, Data Engineering, Statistics, or a related field
- 5+ years in analytics engineering or data engineering, with a track record of production delivery across data systems and at least one AI or LLM project
- Strong Python skills applied to production scripting, pipeline development, and API integration, not research notebooks
- Solid experience with a modern data transformation framework (dbt or equivalent), including data modelling, documentation standards, testing, and CI/CD integration in production
- SQL proficiency and hands-on experience with BigQuery or an equivalent cloud data warehouse, including complex transformation logic and performance-aware query design
- Production experience with a workflow orchestration tool (Airflow or equivalent)
- Hands-on experience with LLM APIs (OpenAI, Anthropic, or equivalent) and practical familiarity with RAG or document retrieval systems
- Strong software engineering practices across version control, CI/CD pipeline design, API design, testing, and code review
- Able to drive cross-functional alignment without direct authority, working across multiple AI-capable teams and business domains
- Clear communicator with both technical and non-technical stakeholders
- Eligible to work in Singapore
Nice to Have
- Experience with agent orchestration frameworks (LangChain, LangGraph, CrewAI, or similar)
- Familiarity with a major cloud platform (GCP, AWS, or Azure) and its managed data services
- Exposure to data catalog or metadata management tools (OpenMetadata, DataHub, Alation, or similar)
- Experience with enterprise LLM platform deployment and access control
- Interest in investment, financial services, or multi-asset trading domains
Work Environment
Attractive compensation package with competitive salary and flexible bonus scheme. Individual career path with management and technical growth tracks, enhanced by learning and development programmes. Healthy work environment with company-sponsored medical programme, open communication, and friendly policies supporting work-life balance. We are an equal-opportunity employer and value diversity.
