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.
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What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
