At a Glance
- Tasks: Design and maintain scalable data pipelines for analytics and reporting.
- Company: Join a diverse and innovative tech company focused on data solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Embrace a dynamic environment with a commitment to diversity and inclusion.
- Why this job: Make an impact by optimising data processes and collaborating with talented teams.
- Qualifications: Proficient in SQL, Python, and cloud platforms; 2 years of relevant experience required.
The predicted salary is between 50000 - 60000 £ per year.
What You'll Be Doing
- Design, build, and maintain scalable data pipelines and data stores supporting analytics, reporting, and operational use cases.
- Develop and optimise ingestion, transformation, and orchestration workflows across structured and unstructured data sources.
- Embed data quality, lineage, reliability, and security controls throughout the engineering lifecycle.
- Collaborate with analysts, data scientists, and business stakeholders to translate requirements into robust data models and reusable datasets.
- Monitor and troubleshoot data jobs, improve pipeline performance, and support production operations and incident resolution.
- Contribute to technical documentation, coding standards, and deployment and testing automation.
What We're Looking For
- Strong proficiency in SQL and data modelling— you can design and optimize schemas for performance and scalability.
- Hands‑on experience building ETL/ELT pipelines—from ingestion through transformation to delivery.
- Solid Python skills and practical experience with cloud data platforms (AWS, Azure, or GCP).
- Minimum 2 years of experience in a data engineering or closely related role.
- Bachelor's Degree in Computer Science, Information Systems, Data Science, or a relevant field.
Good to have
- Experience with Spark or distributed data processing frameworks.
- Familiarity with data warehousing and data lake concepts and architectures.
- Understanding of data governance, security practices, and CI/CD pipelines for data workflows.
Data Engineer in Penarth employer: ACCENTURE PTE LTD
As a Data Engineer at our company, you will thrive in a dynamic and inclusive work environment that champions innovation and collaboration. We offer competitive benefits, opportunities for professional growth, and a commitment to diversity that enriches our workplace culture. Located in a vibrant area, our team enjoys access to cutting-edge technology and resources, ensuring you can make a meaningful impact while advancing your career.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in Penarth
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. You never know who might have a lead or can refer you to a hiring manager.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving SQL, ETL/ELT pipelines, and cloud platforms. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and scenarios. Practice explaining your thought process when designing data models or troubleshooting data jobs—this will help you shine during technical interviews.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it makes it easier for us to keep track of your application and get back to you quickly.
We think you need these skills to ace Data Engineer in Penarth
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Data Engineer role. Highlight your SQL proficiency, ETL/ELT pipeline experience, and any cloud platform knowledge you have. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how your background aligns with our needs. Don’t forget to mention any collaborative projects you've worked on with analysts or data scientists.
Showcase Your Projects:If you've worked on relevant projects, whether in a professional setting or as personal endeavours, include them in your application. We love seeing practical examples of your skills, especially around data pipelines and data modelling.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at ACCENTURE PTE LTD
✨Know Your Data Inside Out
Make sure you brush up on your SQL and data modelling skills. Be ready to discuss how you've designed and optimised schemas in the past, as well as any challenges you've faced with data pipelines. This will show that you not only understand the theory but have practical experience too.
✨Showcase Your ETL/ELT Experience
Prepare to talk about your hands-on experience with building ETL/ELT pipelines. Have specific examples ready that highlight your role in the ingestion, transformation, and delivery processes. This will demonstrate your ability to handle the end-to-end data workflow effectively.
✨Familiarise Yourself with Cloud Platforms
Since cloud data platforms like AWS, Azure, or GCP are crucial for this role, make sure you can discuss your practical experience with them. If you’ve worked with any specific tools or services, be ready to share how they contributed to your projects.
✨Emphasise Collaboration Skills
Data engineering isn't just about coding; it's also about working with others. Be prepared to discuss how you've collaborated with analysts, data scientists, and business stakeholders. Highlight any successful projects where teamwork was key to translating requirements into robust data models.
