Data Consultant

Apollo Solutions
Nottingham
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Consultant - Data Governance

Data Strategy Consultant

Data Analyst/Consultant

Principal Data Consultant - Data Governance

Data Strategy Consultant: AI & Big Data Transformation

Data Strategy Consultant: AI & Big Data Transformation

Principle Data Consultant | Expert Thinking | Azure / AWS | Permanent | Boutique Consultancy | remote-first | strong culture / greenfield Data Projects | Ex-AWS


For Expert Thinking - a thought leader within Cloud and Data and remote-first boutique consultancy - we’re seekingexceptional individualswho embody excellence and demonstrate an unwavering commitment to delivering transformative results. The successful candidate will be avisionary Data Consultantwho thrives in a high-performance environment, possesses an entrepreneurial spirit and has strongcommercial acumento drive pre-sales activities and stakeholder engagement.


The company - Expert Thinking

Expert Thinking is the go-to partner for greenfield Data and Cloud projects in the industry, they help clients to improve cloud & Data maturity, accelerate cloud & Data adoption and drive costs savings. Expert Thinking houses an impressive collective of knowledgeable consultants with backgrounds varying from AWS and UBS to Contino and Pax8.

It's the perfect place to accelerate your growth, enhance your leadership, and sharpen your technical acumen—all while making a meaningful impact on your clients.


Values and Mindset

  • Demonstrates arelentless pursuit of excellenceand continuous improvement
  • Takesfull ownershipof outcomes and consistently exceeds expectations
  • Exhibitsthought leadershipand drives innovation in the data platform space
  • Showsresilience and determinationin overcoming complex challenges


Professional Attributes

  • Possesses agrowth mindsetand actively seeks opportunities to expand capabilities
  • Builds and nurturesstrong relationshipswith clients and team members
  • Approaches problems withcreativity and strategic thinking
  • Maintainscomposure under pressurewhile delivering exceptional results
  • Exceptional communication and stakeholder management skills, able to engage with technical and non-technical audiences


Leadership & Commercial Qualities

  • Acts as amentor and role model, elevating the performance of those around them
  • Drivesstrategic initiativeswith clear vision and purpose
  • Demonstratescommercial acumen, identifying opportunities to deliver business value through data solutions
  • Leadspre-sales engagements, working with customers to define theirdata strategy, architecture, and implementation roadmap
  • Collaborates withsales and business development teamsto create compelling proposals and secure new projects
  • Championsorganizational goalsand inspires others to achieve excellence


Experience & Technical Skills

  • 3+ yearsof experience leadinghigh-performing engineering teamsin a customer-facing and hands-on role
  • Extensive experience buildingperformant, scalable, and secure Azure Data Platformsolutions for enterprise customers
  • Proven experience intechnical pre-sales, guiding customers through defining and implementing solutions that meet their requirements
  • Strongstakeholder engagementexperience, able totranslate complex technical concepts into business value
  • Broad knowledgeof modern data platform solutions acrossmultiple public cloudofferings
  • Expertise in cloud-native engineeringapproaches and methodologies
  • Deep technical expertise withdata models, data mining, and segmentation techniques
  • Proficiency inETL, SQL and e.g.Python, Go or Rfor data manipulation and analysis, with the ability to build, maintain, and deploy sequences of automated processes


Bonus Experience (Nice to Have)

  • Familiarity withdbt, Fivetran, Apache Airflow, Data Mesh, Data Vault 2.0, Fabric, and Apache Spark
  • Experience working withstreaming technologiessuch asApache Kafka, Apache Flink, or Google Cloud Dataflow
  • Hands-on experience withmodern data orchestration toolslikeDagster or Prefect
  • Knowledge ofdata governanceand cataloging tools likeGreat Expectations, Collibra, or Alation
  • Experience inpricing, scoping, and proposal developmentfor data engineering projects


Ready to take your career to the next level, join Expert Thinking!


Apply now!

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 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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.