Customer Data Architect - Associate Manager

WeAreTechWomen
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Databricks Data Engineer & Migration Architect

Data Architect

Lead Data Engineer

Senior Data Engineer (Weymouth based)

Data Engineer

CDI - Junior Data Engineer (Data Science)

Customer Data Architect - Associate Manager

London, Manchester (or other UK location)


Associate Manager (CL8)


Practice: Song Data & AI


About Accenture Song

Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span ideation to execution: growth, product and experience design; technology and experience platforms; creative, media and marketing strategy; and campaign, content and channel orchestration. With strong client relationships and deep industry expertise, we help our clients operate at the speed of life through the unlimited potential of imagination, technology and intelligence. Visit us at https://www.accenture.com/gb-en/about/accenture-song-index


Role Overview

As a Customer Data Architect - Associate Manager, you will help design and implement scalable, secure, and high‑performing customer data architectures. You will work with cross‑functional teams to define data strategies, architect solutions, and enable data readiness for AI, analytics, and customer engagement platforms. This role sits within Accenture’s Data & AI practice, delivering end‑to‑end solutions from data strategy to core engineering.


Key Responsibilities
  • Architect modern customer data platforms and integration pipelines across structured and unstructured sources.
  • Translate business requirements into scalable data solutions aligned with customer engagement goals.
  • Lead the design and implementation of data models, pipelines, and integration frameworks.
  • Support the implementation of Customer Data Platforms (CDPs), MDM, and CRM systems.
  • Ensure compliance with data governance, metadata standards, and data quality frameworks.
  • Collaborate with stakeholders to define data architecture vision and roadmap.
  • Contribute to the development of reusable architecture assets and design patterns.
  • Mentor junior team members and support capability development within the practice.

Required Skills & Experience
  • Strong experience in data architecture, ideally in customer‑centric domains such as CRM, CDP, or marketing analytics.
  • Strong knowledge of cloud platforms (Azure, GCP), data warehousing, and ETL tools.
  • Proficiency in SQL, Python, and data modelling techniques.
  • Experience with customer data platforms (e.g., Salesforce CDP, Redpoint), MDM, and real‑time data processing.
  • Understanding of data privacy, consent management, and regulatory compliance.
  • Excellent communication and stakeholder management skills.

Preferred Qualifications
  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
  • Certifications in cloud platforms (e.g., Azure Data Engineer, GCP Associate Cloud Engineer).
  • Familiarity with AI/ML applications in customer engagement and personalisation.

Why Join Accenture?
  • Work with cutting‑edge technologies and global clients.
  • Be part of a diverse, inclusive, and innovation‑driven culture.
  • Access world‑class learning resources and career development programmes.
  • Join a team that values stewardship, integrity, and client value creation.

What’s in it for you

Our Total Rewards consist of a competitive basic salary, annual performance bonus, opportunities to acquire equity and a wide range of health and wellbeing benefits. These include perks such as:


  • 30 days of leave to spend each year plus 3 extra volunteering days per year for charitable work of choice.
  • Family‑friendly and flexible work policies.
  • Attractive pension plan with financial wellbeing support and resources.
  • Private healthcare insurance plan and Mental Wellbeing support.
  • Employee Assistance Programme, Career Development and Counselling.
  • A range of generous Parental Leave offerings.

Equal Employment Opportunity Statement

All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Accenture is committed to providing veteran employment opportunities to our service men and women. Please read Accenture’s Recruiting and Hiring Statement for more information on how we process your data during the Recruiting and Hiring process.


Locations

London


#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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.