Data Analyst SQL

Jago Consultants
Wokingham
1 month ago
Applications closed

Related Jobs

View all jobs

Business Data Analyst (SQL | Power BI)

Data Analyst ( Power BI / SQL )

Data Analyst Team Lead

Business Intelligence Analyst

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

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

Job Description

Experienced Database Analyst or Data Engineer with SQL experience required


Working within a team of inhouse developers and testers plus cloud operations and support services, you will be empowered to take the client’s product to greater heights and look to the future in supporting the building of a data platform roadmap.


Looking for someone that can make an immediate contribution around supporting, maintaining, tuning and optimising the data that the client’s platform sits upon in ‘lab’ conditions, as observed in our CI / CD pipeline, and when live for on‐premise and cloud customers assist with third line support.


Combined with contributing to the current model, build a road map that looks to further leverage automation, review and update technology / architecture to ensure long term technical viability, provide insight to feed DevOps pipeline, infusing key analytics technologies where appropriate. Prior experience of working through data platform transitions and upgrade desired.


Ultimate responsibility, as part of the team, for ensuring that the solution continues to exhibit high levels of performance, security, scalability, maintainability and reliability.


Skills / Experience

  • Experience working with relational databases (SQL Server and / or Oracle) in a DBA / Data Engineer type role.
  • Query troubleshooting knowledge such as isolating blocks of poor performing SQL, determining root cause, and developing remediation actions.
  • Experience with database management tools.
  • Experience with database performance tuning.
  • Cloud Data Engineer / SysAdmin experience.
  • Experience having worked through a solution’s customer base transitioning from on‐premise / hybrid to cloud
  • Experience with data migration strategies and moving large scale production systems either in on‐ premise / data centre environments or migration to cloud scenarios.
  • Knowledge of cloud development platforms and options.
  • Proven track record in designing, building and maintaining enterprise level applications.

Qualifications

  • A degree or industry recognised equivalent in a computer / numerate subject

Person Profile

Enterprise‐scale technical experience working with relational databases and cloud / hybrid infrastructures, architecture designs and database migrations.


The technical aptitude and experience to learn new / emerging technologies and understand relevant cloud trend.


Someone who can blend the near‐term needs with the long‑term vision and provide leadership on that long term data platform vision.


Package :


Salary depending on Experience level


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.