Analytics Engineering Lead

Analytics Engineering Lead

AI & Analytics Engineering | Singapore

Why This Role Exists

Our data platform is the foundation the business trusts to make decisions, and our AI tools are only as reliable as the platform beneath them. This role exists to achieve two outcomes: a data platform where warehouse changes do not break the tools downstream of them, and an in-house AI layer that lets domain teams get trusted answers from their own data.

At its core this is an analytics engineering role. You own the release process, the warehouse-to-AI connection, and the pipelines that keep our AI workspaces answering from current data, and you build the first in-house agent on top of that foundation. The domain analysts own what the AI knows and whether an answer is right. You own how it runs.

The Impact You Will Have

A data platform the business can rely on

• Warehouse changes ship safely and predictably: you help build and run the release process, with branch protection, CI standards, code review, and healthy orchestration, and help harden it so it holds up without leaning on any single reviewer.
• The warehouse-AI connection stays secure and cost-controlled: you configure and maintain the guardrails for the enterprise AI platform connection, covering access controls, cost monitoring, and security configuration.
• Upstream changes never blindside downstream AI tools: with the change management rhythm you build with data engineering, the impact of a model change is understood before it ships.
• Data models meet a consistent quality bar: the documentation requirements, test coverage, and PR gates you maintain mean analysts stop wondering which tables they can trust.
• Pipeline failures surface before they reach production, not after: the validation you build into orchestration means the team hears about problems from the system, not from a stakeholder with a broken report.

An AI layer that turns data into trusted answers

• A trusted AI knowledge platform you help to build: warehouse connectivity, working with our engineers and domain analysts to build data and document ingestion pipelines
• Business context flows into AI workspaces without manual effort: the ingestion pipelines you build keep institutional knowledge current on a steady cadence, with no manual steps left for analysts.
• Analysts and business teams get an in-house text-to-SQL agent: you build it with our engineering team on an architecture that is documented and can be extended later as needs are agreed.
• AI outputs are checked before they go live: you put accuracy checks in place with the domain analysts, so a workspace is signed off against a known standard before teams rely on it.
• Automated reporting reaches production quality: working with domain analysts, you take reporting from proof-of-concept to production, with data freshness checks, failure detection, and error handling built in.

What Success Looks Like

Illustrative milestones for the first year

• First 90 days: The release process and change management practices are in place, so warehouse changes ship safely and downstream AI tools are protected from upstream surprises.
• By 6 months: The domain workspaces are connected to the AI platform and queryable, and the in-house text-to-SQL agent is in production.
• Within 12 months: Automated reporting runs in production with freshness and failure checks, the platform is stable, and domain teams answer their own questions without an analyst in the loop.

What You Bring

• 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.
• Strong Python 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) in at least one project, production or personal, and the appetite to make this a core part of your job.
• Strong software engineering practices across version control, CI/CD pipeline design, API design, testing, and code review.
• Proven ability to collaborate effectively across technical teams of different cultures; and influence outcomes without direct authority.
• Effective and clear communication in a variety of professional settings (one-on-one, small & large groups, with peers and leaders); with both the technical and non-technical stakeholders.