Solutions Architect - Amazon QuickSight

Amazon
London
1 year ago
Applications closed

Related Jobs

View all jobs

GenAI Solutions Architect: LLMs, MLOps & Product Impact

Senior Data Scientist - AI-First Solutions Architect

AWS Data Engineer

SAS Data Engineer

Azure Data Analyst

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.