Senior Data Engineer - SQL

Fruition Group
Leeds
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Job Title -

Senior Data Engineer - SQL Location -

Hybrid - Leeds (2 days per week onsite) Salary -

£55,000 - £70,000 + Benefits Why Apply?

This is a brilliant opportunity for a skilled Senior Data Engineer to play a key role in delivering robust data solutions for a growing consultancy-led organisation. Working across enterprise-level projects, you'll design and develop modern data platforms using SQL, Power BI, and Azure technologies. This is a full-time Senior Data Engineer role where you'll work closely with cross-functional teams to ensure the successful delivery of business-critical data infrastructure. If you're searching for your next challenge in data engineering, this could be the perfect fit. Senior Data Engineer Responsibilities Design and implement efficient, scalable data pipelines and ETL processes Develop and manage SQL-based data solutions using SSIS, SQL Replication, and Azure Data Factory Build robust data models and dashboards in Power BI to support business intelligence initiatives Collaborate with analysts, developers, and stakeholders to gather requirements and translate into data solutions Maintain and improve data warehousing structures and reporting capabilities Ensure data quality, consistency, and security across systems Optimise performance of data workflows and troubleshoot data-related issues Contribute to data architecture decisions and technical documentation Senior Data Engineer Requirements Proven experience in a Data Engineering or related role, ideally within a consultancy or fast-paced delivery environment Advanced SQL skills with a strong background in database design and optimisation Hands-on experience with SSIS and SQL Replication Proficient in Power BI for dashboard development and data visualisation Experience with Azure Data Factory or similar cloud data integration tools Familiarity with Visual Studio for database projects Strong understanding of data warehousing principles Excellent problem-solving and communication skills Ability to manage multiple priorities and deliver high-quality solutions independently and as part of a team What's in it for me? Competitive salary Flexible hybrid working (2x days per week onsite) 25 days holiday + bank holidays Private healthcare Continuous learning budget and professional development support Exciting project work across multiple industries and domains Supportive and collaborative working culture We are an equal opportunities employer and welcome applications from all suitably qualified persons regardless of their race, sex, disability, religion/belief, sexual orientation or age.

TPBN1_UKTJ

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.