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Pluang is seeking an Analytics Engineering Lead to build and own the data platform and AI infrastructure that powers the company's decision-making. This is a hands-on leadership role combining analytics engineering with AI/ML platform development.
You will own two core outcomes: (1) a reliable data warehouse with safe release processes, change management, and downstream protection for AI tools, and (2) an in-house AI layer enabling domain teams to extract trusted answers from their data.
Key responsibilities include:
- Building and running a production release process for warehouse changes with branch protection, CI standards, code review, and orchestration hardening
- Configuring and maintaining guardrails for enterprise AI platform connections, including access controls, cost monitoring, and security
- Establishing change management practices with data engineering to prevent upstream model changes from breaking downstream tools
- Maintaining consistent data quality standards through documentation, test coverage, and PR gates
- Building validation into orchestration to surface pipeline failures before production
- Developing warehouse connectivity and data ingestion pipelines for AI workspaces
- Building an in-house text-to-SQL agent with engineering and domain analyst teams
- Implementing accuracy checks and production-quality automated reporting with freshness and failure detection
Success milestones: within 90 days, establish safe release and change management processes; by 6 months, connect domain workspaces to the AI platform with a production text-to-SQL agent; within 12 months, achieve stable automated reporting and enable domain teams to self-serve analytics.
Required qualifications: Bachelor's or Master's in Computer Science, Data Engineering, Statistics, or related field; 5+ years in analytics or data engineering with production delivery track record; strong Python for production scripting and pipeline development; expertise with modern data transformation frameworks (dbt or equivalent) including modeling, documentation, testing, and CI/CD; SQL proficiency and hands-on BigQuery or equivalent cloud warehouse experience; production experience with workflow orchestration (Airflow or equivalent); hands-on LLM API experience (OpenAI, Anthropic, etc.); strong software engineering practices (version control, CI/CD, API design, testing, code review); proven cross-functional collaboration and influence without direct authority; excellent communication with technical and non-technical stakeholders; eligibility to work in Singapore.
Nice-to-have: agent orchestration frameworks (LangChain, LangGraph, CrewAI); major cloud platform experience (GCP, AWS, Azure); data catalog/metadata tools (OpenMetadata, DataHub, Alation); enterprise LLM platform deployment; RAG or document retrieval systems; interest in fintech or trading domains.
