Analytics Engineering Lead
pluang technologies pte. ltd.
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Benefits offered by this job
Job summary
Pluang Technologies Pte. Ltd. is seeking an analytics engineer to own the release process, data warehouse to AI connections, and the pipelines that keep AI workspaces current. You will build the first in‑house agent on top of that foundation and ensure downstream tools stay accurate.
You will collaborate with domain analysts and engineers to deliver production‑grade data models, tests, and documentation, using dbt, BigQuery, Airflow, and LLM APIs, with a strong focus on reliability and security.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or related field.
- 5+ years in analytics engineering or data engineering with production delivery experience.
- Strong Python for production scripting, pipelines, and API integration.
- Experience with modern data transformation framework (dbt) including modeling, docs, tests, and CI/CD.
- SQL proficiency with BigQuery or equivalent cloud warehouse and complex transformations.
Responsibilities
- Own the release process and change management for data pipelines and AI integration.
- Build data and document ingestion pipelines to feed AI workspaces.
- Ensure data models meet quality with docs, tests, and PR gates.
- Implement validation and monitoring to detect issues before production.
- Collaborate with domain analysts and engineers to deliver production-grade reporting.
Skills
Education
Tools
Job description
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.
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.
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.
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.
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.
Good interpersonal skills; with a record of building and maintaining rapport with stakeholders.
Eligibility to work in Singapore.
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.
Practical familiarity with RAG or document retrieval system.
Interest in investment, financial services, or multi‑asset trading domains.
We invest in the growth of those who drive our mission forward:
Health & Wellness: Comprehensive medical insurance, meal stipends, collaborative team events, and mobile fitness benefits.
Financial Well‑being: Competitive compensation, performance‑linked yearly incentives, and relocation support.
Career Development: Clear progression tracks and the autonomy to pivot your expertise across different businesses and functions.
Flexibility & Time Off: Hybrid work arrangements and comprehensive paid leave, recognizing that sustainable impact requires rest.
Careers at Pluang grow in ways you would not always predict, because the ownership is real, the scope is wide, and the people around you push you to think differently.
You will eventually find yourself steering high‑stakes decisions, partnering across global domains, and building a track record judged by what you change rather than how much you did.
We do not promise a linear path. We promise a meaningful one.
You possess a founder's mindset and the courage to act. You are committed to the integrity of your work and thrive in a setting where peer support and intellectual challenge exist in equal measures.
If you are prepared to do the work that matters and compounds, come help us build the future of finance.
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