Data Architect

SYSTEMIQ
Leeds
3 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Landscape Assessment

  • Conduct stakeholder interviews to identify current data sources, workflows, and platforms.
  • Map existing data flows and assess data quality, governance, and accessibility.
  • Benchmark existing tools and systems against best-practice solutions in similar ecosystems.

Future-State System Design

  • Define an ideal data architecture that enables seamless integration, governance, and analytics.
  • Create high‑level design documentation aligned with strategic goals and operational needs.
  • Recommend approaches for interoperability, scalability, and security.

Business Requirements & Procurement Support

  • Translate user needs into clear, prioritised technical and functional requirements.
  • Contribute to the development of an RFP to engage a qualified system developer.
  • Advise on vendor evaluation criteria and selection processes.

Stakeholder Engagement

  • Facilitate alignment across technical and non‑technical stakeholders.
  • Build consensus on requirements, timelines, and governance models.

Qualifications

  • 5+ years' experience in designing and implementing data system architecture.
  • Proven track record in multi‑stakeholder data governance, access control, and interoperability.
  • Proficiency in API integration and data exchange standards.
  • Strong analytical, problem‑solving, and communication skills.
  • Business/Native Fluency in English.
  • Experience with agri‑commodity supply chain, sustainability, or field‑level data collection systems.
  • Understanding of GIS, traceability, or certification systems.
  • Proficiency in additional languages; French particularly welcomed.

Commitment & Location

  • Commitment: 70‑100% (25‑40 hrs / week), approx. 3 months with potential extension.
  • Location: Remote with potential for travel as required.

Company Description

Systemiq is a system‑change company committed to achieving the Sustainable Development Goals and the Paris Agreement by transforming markets and business models in five key systems: nature and food, materials and circularity, energy, urban areas, and sustainable finance. As a certified B Corp, we combine strategic advisory with high‑impact, on‑the‑ground work, partnering with business, finance, policy‑makers, and civil society to deliver system change. Since our start in 2016, we have grown to more than 350 people working across locations in Brazil, Germany, France, Indonesia, Netherlands, UK and the US. We are a pure‑play sustainability‑focused company, dedicated exclusively to sustainability projects.


What We Do

Systemiq orchestrates system change in clean energy, circular material solutions, and sustainable land use. This involves re‑imagining industries, re‑configuring the energy world, and regenerating ecosystems to address systemic failures, unlocking economic opportunities that benefit business, society, and the environment.


Diversity & Inclusion

  • Embedding diverse and inclusive practices in all aspects of our work, including recruitment, performance management, and people processes and policies.
  • Providing a supportive environment where everyone can be themselves and perform at their best, regardless of background, personality, gender, sexual orientation, race, mental health, religion, or other characteristics.
  • Supporting our people through all life stages, accommodating personal priorities, and promoting a sustainable approach to work and life.


#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.