Overview

About Amplify Health

Responsibilities

• 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)
• 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.
• 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.
• 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.
• 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.
• 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.

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.
• 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 such as HL7 FHIR and regional privacy regulations (for example, PDPA, PDPO, HIPAA, GDPR).
• Experience setting up AI governance (AI system inventory, risk tiering, model and agent registers) or an AI centre of excellence.
• Multi-cloud (AWS, GCP) experience and Snowflake or Microsoft Fabric interoperability.
• Thought leadership: talks, publications, open-source contributions or partner advisory boards.
• Consulting, product management or business qualifications (for example, an MBA).
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science or a related field, or equivalent practical experience.
• Microsoft Certified: Azure Solutions Architect Expert (AZ-305).
• Databricks Certified Data Engineer Professional and Databricks Certified Generative AI Engineer Associate.
• IAPP Artificial Intelligence Governance Professional (AIGP) or ISO/IEC 42001 Lead Implementer (desirable).
• TOGAF or an equivalent architecture certification (optional).
• Trusted advisor: clients and executives seek your view because you are candid about value, risk and trade-offs.
• Multiplier: you raise the capability of every team through standards, reusable assets and coaching of leads.
• Hands-on credibility: you can still go deep on architecture and code when it matters.
• Accountability: you own outcomes across the portfolio, including responsible and secure use of AI.
• Portfolio delivery: projects delivered on scope, quality, timeline and budget, with no unmanaged critical risks.
• Client outcomes: client satisfaction, renewals and expansions, and AI use cases moved from pilot to production with measured business value.
• Reusable assets: accelerators and reference architectures adopted across projects, reducing delivery time and cost.
• Responsible AI: all AI solutions pass release gates and governance reviews, with no material data or AI incidents.
• People: engaged teams, leads developed and promoted, and strong growth in skills and certifications.
• Thought leadership: active communities of practice and external talks or publications.