Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Solutions Architect - Amazon QuickSight

Amazon
London
8 months ago
Applications closed

Related Jobs

View all jobs

Deep Learning Solutions Architect – Inference Optimization

Deep Learning Solutions Architect – Inference Optimization

Deep Learning Solutions Architect – Inference Optimization

Solution Architect – AI & Machine Learning

Professional Services Manager (Data Engineering Team)

Full Stack Data Engineer

Job ID: 2826021 | Amazon Web Services Australia Pty Ltd

Within the Gen AI/ML Specialist organization, this position is part of the Worldwide Specialist Solution Architecture Team, where you will join a global team for Amazon QuickSight (inclusive of Amazon Q). You will help guide customers in their adoption of Q and QuickSight through the creation of scalable enablement mechanisms, deep dive technical guides, and 1:1 engagement with customers as they evaluate service capabilities. You partner with technical and field teams across AWS and bring the voice of the customer into our product development roadmap.


Key job responsibilities

  1. Design and develop solutions and prototypes for customers that make the best use of Amazon QuickSight, including Generative BI capabilities of Amazon Q in QuickSight, and educate customers on how to integrate dashboards and Q&A experiences into their custom applications.
  2. Collaborate with AWS field sales, training, and support teams to ensure customer success.
  3. Create reusable customer content, such as demos, presentations, documentation, blogs, etc., that will drive adoption of Amazon QuickSight.
  4. Act as technical liaison between customers and the service engineering teams, providing product improvement feedback to AWS developers and accelerating the adoption of new features in customer deployments.
  5. Share what you know by capturing best-practice knowledge from engineering and field teams, including reference architectures and patterns amongst the worldwide AWS solution architect community in order to build a strong worldwide database, analytics and AI/ML community.
  6. Evangelize AWS services and solutions that benefit customers and publicly speak at events such as AWS Summits and AWS re:Invent.


A day in the life

Your daily schedule will be a mix of solving customers' challenges and creating scalable assets to further promote the knowledge and understanding of Amazon QuickSight and Amazon Q in QuickSight. You will collaborate with field teams to drive successful customer outcomes, as well as collaborate with service teams to incorporate customers' feedback into the roadmap. You will work closely with Go-To-Market (GTM) Specialist counterparts to develop a strategy to drive customer adoption further in your territory through 1:1 customer engagement and 1:many virtual and in-person events.


BASIC QUALIFICATIONS

  1. 4+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.
  2. 3+ years of design, implementation, or consulting in applications and infrastructures experience.
  3. Bachelor's degree.
  4. 2+ years of experience developing dashboards with a Business Intelligence platform such as QuickSight, Tableau, PowerBI, Looker, Thoughtspot, Microstrategy, Sisense, Domo, etc.


PREFERRED QUALIFICATIONS

  1. Experience in technology/software sales, pre-sales, or consulting.
  2. Experience with scripting (e.g. Python, PowerShell).
  3. Experience with AWS technologies.
  4. 2+ years experience architecting and implementing Business Intelligence solutions into production.


Acknowledgement of country:
In the spirit of reconciliation Amazon acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.


IDE statement:
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer, and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected attributes.


Posted:November 19, 2024 (Updated 10 days ago)

#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.