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DysrupIT

Data & AI Architect

DysrupIT

. Lead pre-contract technical solutioning and subsequent delivery, preserving continuity from proposal to production .

Posted 9/21/2026full-timeRemote • PhilippinesSeniorLeadWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading technical solutioning and delivery for data and AI projects, with a strong focus on Databricks lakehouse architecture, generative AI systems, and client engagement. Proven ability to mentor teams, implement best practices in DevOps/MLOps, and produce high-quality technical documentation.

Highest-signal resume keywords
Databricks Lakehouse ArchitectureGenerative AI Systems DevelopmentProduction Python and SQLDevOps/MLOps FundamentalsClient-Facing Consulting Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
DatabricksDelta LakeUnity CatalogSpark/PySparkCI/CDInfrastructure as CodeData ModellingPerformance OptimisationOrchestrationCost Optimisation
Soft Skills
Excellent Written CommunicationPresentation SkillsMentoring
Tools & Technologies
Azure Data FactoryAzure OpenAIPower BISnowflakeDbt
Certifications & Qualifications
Databricks ProfessionalAzure Solutions Architect Expert
Industry Keywords
Financial ServicesRetailTravelResponsible AI GovernanceBig Four Consulting

Tech Stack

Tools & technologies
AzurePySparkPythonSparkSQLUnityVault

About the role

Key responsibilities & impact
  • Lead pre-contract technical solutioning and subsequent delivery, preserving continuity from proposal to production
  • Split time approximately equally between client engagement and delivery, flexing with the pipeline
  • Shape technical approaches, challenge problem statements and facilitate discovery workshops
  • Produce target-state architectures, build sequences, proposal assumptions, exclusions, risks and defensible estimates
  • Design four-to-six-week proofs of concept and serve as technical peer to client architects, data leaders and CIOs
  • Own end-to-end architecture and, where required, lead delivery, scope, stand-ups and client technical activities
  • Implement demanding components and reference solutions hands-on
  • Deliver Databricks lakehouses, including medallion layers, Unity Catalog, Delta Lake, ingestion and orchestration
  • Build production generative AI systems covering RAG, agents, evaluation, prompt/context engineering, cost and latency
  • Set CI/CD, infrastructure-as-code, testing, observability and cost standards
  • Mentor client engineers and manage production readiness and handover
  • Turn delivery experience into reference architectures, accelerators, templates and estimation models
  • Contribute to Frontier Academy and maintain current recommendations across Databricks, Microsoft and Anthropic
  • Help shape and eventually lead a small delivery team, including recruitment

Requirements

What you’ll need
  • About eight years in data/AI engineering and architecture, including three years with substantive design authority and senior client-facing consulting exposure
  • Databricks experience: lakehouse architecture, Delta Lake, Unity Catalog, Spark/PySpark, Lakeflow or Delta Live Tables, orchestration, performance and cost optimisation
  • Azure/Microsoft experience: Data Factory or Fabric, ADLS, Azure OpenAI or AI Foundry, Entra ID and networking
  • Production generative AI experience with RAG, vector stores, agents/tool use, evaluation, guardrails and prompt/context engineering, including Claude or an equivalent frontier model
  • Strong production Python and SQL
  • Sound data-modelling judgement across dimensional, data vault and wide denormalised approaches
  • DevOps/MLOps fundamentals: version control, CI/CD, infrastructure as code, containers and monitoring
  • Excellent written and spoken English for executive proposals, decision records and presentations
  • Databricks Professional or Azure Solutions Architect Expert certification (desirable)
  • Big Four, global systems integrator or specialist consultancy experience, including bids, statements of work and estimation (desirable)
  • Applied responsible AI governance and delivery experience in financial services, retail or travel (desirable)
  • Experience with Australian/APAC clients, Snowflake, dbt, Power BI or Fabric, and mentoring small engineering teams (desirable)

Benefits

Comp & perks
  • No benefits, perks, or compensation extras are specified