Senior Full Stack Data Engineer (Client Facing)

Decho Group
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Lead Data Scientist

Lead Data Scientist

Lead Data Engineer

Data Analyst – Demand Planning & Supply Chain

Senior Full Stack Data Engineer – Decho Group (Part of Accenture)


About Decho Group


Decho Group is a fast-growing Tech and AI consultancy, founded to solve complex data challenges for governments and enterprises. We specialise in Palantir technologies, advanced analytics, and AI-driven solutions that transform how organisations make decisions.


In October 2025, Accenture acquired Decho Group, recognising our unique ability to combine deep engineering expertise with strategic advisory, tackling mission-critical problems in defence, healthcare, and commercial industries.


Joining Decho means joining a consultancy where AI meets engineering excellence. You’ll be part of a team that thrives on curiosity, collaboration, and bold thinking, working on projects that genuinely change lives and industries.


The Role

We’re seeking Senior Consultant Engineers who want to apply their technical mastery in AI consultancy and Palantir engineering while shaping the future of data-driven decision-making.


This is a hands-on, client-facing role where you’ll lead technical delivery, partner directly with stakeholders, and act as a trusted advisor. You’ll combine software engineering expertise with strategic problem-solving, working across data pipelines, operational workflows, and AI models to deliver transformation at scale.


Key Responsibilities

  • Lead & Architect: Own the design of innovative solutions using Palantir software, ensuring scalability and long-term impact.
  • Engineer Data at Scale: Build, optimise, and maintain complex pipelines and ETL processes powering mission-critical decision models.
  • Strategic Workflows: Develop enterprise-grade workflows and decision-support tools that reshape operations across industries.
  • AI Leadership: Guide the integration of AI and machine learning models into client environments, ensuring measurable outcomes.
  • Client Engagement: Partner directly with senior stakeholders, translate business needs into technical solutions, and influence strategic direction.
  • Technical Excellence: Set engineering standards, champion best practices, and drive continuous improvement across teams.
  • Mentorship & Growth: Coach junior engineers, foster knowledge-sharing, and contribute to the Decho Lab’s innovation culture.


What We’re Looking For

  • 5+ years of experience in software/data engineering with proven expertise in Python, SQL, and TypeScript.
  • Ideally experience with Palantir technologies (Foundry, Gotham, or similar platforms).
  • Advanced knowledge of data engineering, ETL pipelines, and workflow design at enterprise scale.
  • Track record of leading teams, mentoring engineers, and delivering complex projects in client-facing environments.
  • Passion for AI, machine learning, and emerging technologies, with ability to translate innovation into impact.
  • Exceptional problem-solving, communication, and stakeholder management skills.
  • Curiosity, adaptability, and a drive to make a real-world impact.


Why Join Us

  • Be part of a fast-growing AI consultancy now backed by Accenture’s global scale.
  • Lead mission-critical, client-facing projects across government, defence, health, and commercial sectors.
  • Gain hands-on experience in AI, advanced analytics, and Palantir technologies while shaping industry standards.
  • Build your career story as a senior leader driving transformation in data-driven decision-making.
  • Thrive in a culture that values innovation, collaboration, and bold ambition.



Unfortunately we can not provide sponsorship for this opportunity

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.