Lead agentic delivery and large-scale data migration/modernisation programs, institutionalise DataOps and CI/CD, design multi-agent workflows for SDLC acceleration, ensure security and responsible AI controls, and manage large cross-functional agile teams and client engagements.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
Key Role & Responsibilities
1) Agentic Delivery Leadership (LLM + Multi‑Agent)
- Define and lead the agentic delivery vision and roadmap for data engineering / platform modernisation engagements.
- Design multi‑agent workflows to accelerate delivery across the SDLC (e.g., requirements decomposition, code generation, test generation, review assistance, runbook creation, incident triage support).
- Establish standards for prompt engineering, agent orchestration, evaluation, and quality gating (accuracy, hallucination controls, regression safety).
- Create reusable accelerators, templates, and reference implementations for delivery teams.
2) Data Migration & Modernisation Program Delivery
- Own end‑to‑end delivery for large data migration / modernisation programmes (on‑prem → cloud, legacy DW → lakehouse/warehouse, ETL → ELT).
- Translate business goals into a delivery plan: milestones, sprint plans, dependency management, RAID, release strategy.
- Drive engineering excellence for ingestion, transformation, modelling, governance, and consumption layers (semantic/BI enablement where needed).
- Ensure performance, scalability, reliability, and cost governance are built into designs (not bolted on later).
3) DataOps, CI/CD and SDLC Acceleration
- Institutionalise DataOps practices: CI/CD for pipelines, automated testing, data quality checks, observability, and secure deployments.
- Implement “shift‑left” quality via automated checks (unit, integration, data validation, performance) and agentic support to reduce cycle time.
- Standardise documentation artefacts (architecture, test evidence, runbooks, SOPs) and automate generation where practical.
4) People, Agile & Stakeholder Leadership
- Lead and mentor large cross‑functional agile teams (engineering, QA, platform, analysts), building a culture of ownership and continuous improvement.
- Facilitate agile ceremonies and delivery governance; coach scrum teams to improve velocity without compromising quality.
- Be a client‑facing leader: run workshops, communicate trade‑offs, manage expectations, and provide roadmap visibility.
5) Security, Risk & Responsible AI
- Establish controls for data security, privacy, and compliance when using LLMs/agents (data handling, access controls, logging, secrets management).
- Define guardrails for safe usage: redaction, grounded responses (RAG patterns where needed), approval workflows, and auditability.
Must Have (Core Requirements)
- 20+ years overall experience in data engineering / platform delivery, including large-scale migration/modernisation programmes.
- 10+ years experience leading large delivery teams (multi‑pod agile) and driving complex client outcomes.
- Strong hands‑on foundation in data engineering concepts: data modelling, pipeline design, testing strategy, performance tuning, and production support.
- Proven experience implementing DataOps/CI/CD practices for data platforms (version control, automated testing, release management).
- Practical experience with LLMs and applied GenAI in engineering workflows (tool use, agent patterns, evaluation, governance).
- Strong client management skills: requirements workshops, solution options, trade‑offs, and delivery roadmap execution.
- Excellent communication skills—able to explain complex technical approaches to both technical and non‑technical stakeholders.
Good to Have (Preferred)
- Experience with cloud data platforms and modern stacks (any of Azure/AWS/GCP; lakehouse/warehouse ecosystems).
- Exposure to multi‑agent orchestration frameworks and/or building internal developer platforms / accelerators.
- Experience implementing governance patterns: RBAC, masking, row/column security, encryption, secure sharing.
- Domain exposure across industries (BFSI, Insurance, Healthcare, Retail, etc.) and leading distributed global teams.
Similar Jobs
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Lead enterprise AI transformation and architecture engagements, advising C-suite on AI strategy, designing end-to-end AI and data architectures (RAG, knowledge graphs, data catalogs), defining governance and responsible AI frameworks, and driving adoption, enablement, and practice development across ServiceNow implementations.
Top Skills:
Agent-To-Agent (A2A)Agentic WorkflowsAi AgentsAi Control TowerApp EngineCsmFsmGenerative Ai Skill KitHrsdItsmKnowledge GraphsModel Context Protocol (Mcp)Now AssistRdfRetrieval-Augmented Generation (Rag)ServicenowSparql
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Lead and set technical direction for a cloud-native platform across multiple teams, solving multi-cluster, multi-cloud, control-plane, workload isolation, identity, and reliability problems. Drive architecture decisions, build critical control-plane components, mentor senior engineers, and influence hyperscaler strategy and platform standards at scale.
Top Skills:
AksAWSAzureCni (Container Network Interface)CrossplaneEksGCPGitopsGkeGoInfrastructure-As-CodeKata ContainersKubernetesKubernetes OperatorsMetricsOci BundlingService MeshSlosSpiffeSpireTracing
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Design, build, and maintain multicloud infrastructure and CI/CD for CPQ microservices. Develop and test Python applications, deploy containers on Docker/Kubernetes, manage GCP/Azure/AWS environments, integrate AI/ML microservices, and ensure security, monitoring, and operational reliability. Mentor engineers and provide production support for SaaS CPQ solutions.
Top Skills:
Ai/MlAWSAzure Government Community Cloud (Gcc)DockerGitGithub ActionsGoogle Cloud Platform (Gcp)KubernetesAzurePostgresPython
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
