Operations Data Analyst & Administrator

Phillips 66
Immingham
4 months ago
Applications closed

Related Jobs

View all jobs

Trainee Data Analyst - Training Course

Trainee Data Analyst - Training Course

Trainee Data Analyst/Support – Training Course

Trainee Data Analyst

Trainee Data Analyst

Data Analyst Training Course (Excel, SQL & Power BI)

Phillips 66 & YOU – Together we can fuel the future

Phillips 66 has been operating in the UK for over 65 years and we are as excited about our future as we are proud of our past. We are committed to improving lives, and that is our promise to our employees and communities. We are sustained by the backgrounds and experiences of our diverse teams, which reflect who we are, the environment we create and how we work together.


Our company is built on values of safety, honour and commitment. We call our cultural mindset Our Energy in Action, which we define through four simple, intuitive behaviours: we work for the greater good, create an environment of trust, seek different perspectives and achieve excellence.


Our employees are the heart of our success, and there is a reason why we continue to attract great talent. It’s not just the excellent benefits package or the opportunities for personal growth – it’s also the caring and committed culture of the organisation that makes everyone feel like they can bring their authentic self to work and be truly part of our team.


Providing Energy. Improving Lives
About the Role

Are you passionate about data, digital transformation, and operational excellence? Phillips 66 is seeking an enthusiastic and proactive Operations Data Analyst & Administrator to join our Operations team at the Humber Refinery. As the primary contact and digital specialist for operations personnel, you’ll provide business support, drive data analytics, and champion automation and AI adoption across our refinery operations. This is a fantastic opportunity to make a real impact by improving efficiency, streamlining processes, and enabling future‑ready digital solutions.


Note: this is an office‑based role at the Humber Refinery, North Lincolnshire. Relocation support is not available for this position.


Key Responsibilities

  • Collaborate with business teams to identify and deliver efficiency improvements.
  • Develop and automate dashboards and reports using Power BI and other enterprise data tools.
  • Prepare, model, and publish datasets for Operations, ensuring data is presented in clear, actionable formats.
  • Maintain and troubleshoot site software (field mobile data capture, data historian, shift handover system).
  • Manage Operations intranet home pages, SharePoint sites, and AD groups.
  • Support the migration of data to cloud services for future AI adoption.
  • Organise meetings, room bookings, and departmental procurement.
  • Work with HR on upgrades and changes to time and attendance systems, providing end‑user support and liaising with vendors.

Who We’re Looking For

  • Structured, organised, and logical thinker.
  • Proficient in Microsoft Office and Power BI, with a talent for data visualisation.
  • Awareness of AI tools (e.g., Microsoft Copilot, ChatGPT) and an interest in responsible AI practices.
  • Self‑motivated, able to work independently and as part of a team.
  • Excellent planning, organisational, and communication skills.
  • Ability to engage non‑technical stakeholders and lead change initiatives.
  • Flexible and confident in dealing with suppliers, vendors, peers, and managers.

Why Join Us

At Phillips 66, you’ll be part of a forward‑thinking team driving digital transformation in the energy sector. We offer a dynamic work environment, opportunities for professional growth, and the chance to make a meaningful difference.


To be considered

In order to be considered for this position you must complete the entire application process, which includes answering all prescreening questions and providing your eSignature on or before the requisition closing date of 11th November 2025.


Phillips 66 is an Equal Opportunity Employer

Phillips 66 has more than 140 years of experience in providing the energy that enables people to dream bigger and go farther, faster. We are committed to improving lives, and that is our promise to our employees and our communities. We have been recognized by the Human Rights Campaign, the U.S. Department of Labor, and the Military Times for our continued commitment to inclusive practices and policies in the hiring and retention of LGBTQ+ individuals and military veterans. Our company is built on values of safety, honour and commitment. We call our cultural mindset Our Energy in Action, which we define through four simple, intuitive behaviours: we work for the greater good, create an environment of trust, seek different perspectives and achieve excellence.


Learn more about Phillips 66 and how we are working to meet the world's energy needs today and tomorrow, by visiting phillips66.com.


#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.