Staff Data Engineer

Bazaarvoice Ltd
Belfast
3 days ago
Create job alert

At Bazaarvoice, we create smart shopping experiences. Through our expansive global network, product-passionate community & enterprise technology, we connect thousands of brands and retailers with billions of consumers. Our solutions enable brands to connect with consumers and collect valuable user-generated content, at an unprecedented scale. This content achieves global reach by leveraging our extensive and ever-expanding retail, social & search syndication network. And we make it easy for brands & retailers to gain valuable business insights from real-time consumer feedback with intuitive tools and dashboards. The result is smarter shopping: loyal customers, increased sales, and improved products. The problem we are trying to solve : Brands and retailers struggle to make real connections with consumers. It's a challenge to deliver trustworthy and inspiring content in the moments that matter most during the discovery and purchase cycle. The result? Time and money spent on content that doesn't attract new consumers, convert them, or earn their long-term loyalty. Our brand promise : closing the gap between brands and consumers. Founded in 2005, Bazaarvoice is headquartered in Austin, Texas with offices in North America, Europe, Asia and Australia. Its official: Bazaarvoice is a Great Place to Work in the US , Australia, India, Lithuania, France, Germany and the UK! Who we want: Are you ready to combine your talent for crafting solid data systems and enthusiasm for cutting-edge technology to harness the power of data at Bazaarvoice? Were looking for a strong data engineer who thrives on building large-scale, robust, distributed data systems and pipelines, who understands the importance of good software engineering practices to get it done. If youre excited about shaping the future of data at Bazaarvoice, come join us. How you will make an impact: As a key member of the Insights team, you'll be tasked with designing, building, and supporting large-scale, distributed data systems that drive our organization's data infrastructure forward, and power our products and services. Your responsibilities will include developing data pipelines, optimizing data storage and retrieval processes, and ensuring the reliability and scalability of our data architecture. You'll collaborate closely with cross-functional teams to understand data requirements, implement solutions, and troubleshoot issues as they arise. You'll also play a pivotal role in advocating for and implementing software engineering best practices to ensure the efficiency, maintainability, and robustness of our data systems. This role offers an exciting opportunity to work on cutting-edge technology and contribute to shaping the future of data-driven decision making within our organization.

Related Jobs

View all jobs

Staff Data Engineer

Staff Data Engineer

Staff Data Engineer

Staff Data Engineer - Hybrid/Remote with Impact

Staff Data Engineer

Staff Data Engineer

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.