Senior Lead, AI Data Engineer

About Amplify Health
Who We Are
Amplify Health Asia Pte. Limited (Amplify Health) is a pan-Asian health technology and analytics company which brings together health technology assets, proprietary data analytics and healthcare expertise to support innovation across the healthcare value chain.
Headquartered in Singapore, Amplify Health operates in 10 markets across the region: Australia, Hong Kong, India, Indonesia, Malaysia, New Zealand, the Philippines, Singapore, Thailand and Vietnam.
Amplify Health develops integrated solutions that empower healthcare payors, providers and policymakers across Asia to identify value and drive new partnerships, understand variation and address systemic inefficiencies—with the aim of improving outcomes for individuals and the sustainability of health systems.
Powered by a clinically enriched data foundation, Amplify Health’s health intelligence platform combines modular AI solutions, context-aware automation and deep regional insights to offer localised, AI-powered health solutions spanning health payment integrity to chronic disease management. The company’s technology has earned recognition at several industry competitions, including the Celent Model Insurer Awards, ITC Asia Awards and SBR Technology Excellence Awards.
As an ISO27001 certified organisation, Amplify Health maintains clearly defined policies, structured oversight mechanisms and ongoing risk assessments that ensure alignment with global best practices on enterprise data security.
Our Vision and Ambition
To build the leading healthcare AI and platform services company in Asia that transforms the delivery of health and wellness for patients and communities by combining and leveraging the distinctive and complementary assets and strengths of AIA and Discovery.
Amplify Health will simplify access to health data and AI Innovation to accelerate distinct and disruptive healthcare value insights and resulting improvements in health outcomes through value-based care, personalised care plans and aligning individuals’ lifestyle/ behavioural choices.
By 2028, Amplify Health will have in place one of Asia’s strongest health-tech and AI capabilities; a comprehensive, integrated health technology stack supported by precision insights derived from proprietary data pools.
The Position
Role Overview
As Senior Lead, AI Data Engineering, you will lead multiple teams and projects that deliver data and AI solutions on Azure and Databricks, and act as a trusted technical advisor to clients and senior stakeholders. You stay hands-on, shaping architectures, building reference implementations and reviewing critical code, while setting the technical direction, delivery standards and talent strategy for AI data engineering. Your job is to turn AI ambition into governed, measurable outcomes: help clients choose the right use cases, design the data foundations to support them, and make sure they run reliably and responsibly at scale.
Role at a Glance
Experience: 12+ years in data engineering or data architecture, including 4+ years leading multiple teams and 3+ years delivering AI/ML or GenAI solutions
Team leadership: Leads multiple teams or pods (typically 8 to 20 engineers, including lead engineers); line-manages leads and senior engineers
Project scope: Accountable for multiple concurrent projects or programmes (typically 3 or more)
Stakeholder / client exposure: Client-facing trusted advisor; leads technical solutioning, pre-sales and executive engagement
Hands-on expectation: About 30 to 50% hands-on (architecture, reference implementations, critical code and reviews, proofs of concept)
Responsibilities
Strategy and architecture
Define the AI data engineering vision, reference architectures and reusable accelerators (for example, RAG ingestion frameworks, evaluation harnesses, MCP / agent data-access patterns and governance templates) used across projects.
Act as design authority across projects: review and approve solution architectures, key trade-offs and non-functional requirements (security, scalability, cost, resilience, disaster recovery).
Maintain a technology radar for data and AI (lakehouse, agentic AI, evaluation, AI gateways, open table formats) and lead structured evaluations of new platforms and vendors.
Stay hands-on: build reference implementations and proofs of concept, lead reviews of critical production incidents, and review high-risk code and designs.
Client and business engagement
Serve as the senior technical counterpart for clients and business executives: shape AI and data roadmaps and prioritise use cases by value, feasibility and risk.
Lead technical solutioning for pre-sales, proposals and statements of work: scope, architecture, estimates, assumptions, client dependencies and risks.
Present to executive and board-level audiences, translating technical choices into business impact, risk and cost.
Build long-term client relationships and identify opportunities to expand and improve engagements.
Delivery leadership
Oversee delivery of multiple concurrent projects, ensuring quality, timelines, budget and margin, and resolving escalations.
Establish delivery governance: architecture reviews, AI release gates (evaluation thresholds, red-teaming, guardrails), production-readiness reviews and post-incident reviews.
Drive FinOps for data and AI: forecast, optimise and report platform, GPU and token costs.
Governance, risk and responsible AI
Own responsible-AI and AI-risk practices across the portfolio, aligned with frameworks such as the NIST AI RMF, ISO/IEC 42001, Singapore's Model AI Governance Framework for Agentic AI and the EU AI Act where applicable.
Work with legal, compliance, data protection and security teams on AI risk assessments, data residency, consent, auditability and the limits of agent autonomy.
Ensure sensitive data, including health and personal data, is protected by design across every solution.
People and capability leadership
Lead, manage and develop multiple teams, including lead engineers: set goals, run performance reviews and plan succession.
Own the skills framework and learning roadmap for AI data engineering: identify skill gaps, sponsor certifications and run communities of practice.
Lead hiring strategy and team design (pod structure, skill mix, location mix).
Set the standard and policy for AI-assisted and agentic engineering across teams, balancing productivity with quality, security and IP protection.
Build a culture of engineering excellence, psychological safety, learning and innovation.
Candidate Profile
Required Qualifications
12+ years in data engineering or data architecture, with 4+ years leading multiple teams or a practice of 8+ engineers, including leads.
Expert, current hands-on skills in Python, PySpark and SQL, Azure and Databricks lakehouse engineering, and distributed system design.
A track record of delivering multiple GenAI / ML solutions to production at enterprise scale, including RAG or agentic systems, evaluation frameworks and LLMOps.
Deep data and AI architecture expertise: lakehouse, streaming, unstructured data, vector retrieval, semantic layers, model serving, AI gateways and MCP-based agent integration.
Strong grounding in data governance, privacy, security and responsible AI in regulated environments.
Proven client-facing experience: pre-sales solutioning, estimation, statement-of-work inputs, executive presentations and managing client expectations.
Experience managing multi-project delivery, budgets and risks.
Proven people leadership: hiring, performance management, coaching leads and building learning programmes.
Exceptional communication and influencing skills with technical, business and executive audiences.
Technology stack: Primary stack (Azure + Databricks)
Cloud & storage: ADLS Gen2, Azure Data Factory, Event Hubs, Azure SQL, Cosmos DB, Key Vault, Microsoft Fabric / OneLake
Lakehouse & processing: Azure Databricks: Delta Lake, Lakeflow (Connect, Spark Declarative Pipelines, Jobs), Structured Streaming, Photon, serverless compute
Governance & catalog: Unity Catalog (lineage, tags, ABAC, row/column security), Microsoft Purview
AI, retrieval & agents: Databricks Vector Search, Model Serving, Agent Bricks, Lakebase; Azure AI Search; Microsoft Foundry (Azure OpenAI and other models, Agent Service); Azure AI Document Intelligence
MLOps / LLMOps: MLflow 3 (tracking, tracing, evaluation, prompt registry), Feature Store, Unity AI Gateway, inference tables
Engineering & DevOps: Python, PySpark, SQL, Git, GitHub Actions or Azure DevOps, Terraform / Bicep, Databricks Asset Bundles, Docker
AI-assisted engineering: GitHub Copilot, Databricks Genie Code / Assistant
Nice to have
Industry depth in healthcare, insurance or financial services; familiarity with data standards

Work arrangement
Hybrid