At a Glance
- Tasks: Build and manage data pipelines in a remote, flexible role.
- Company: Join a dynamic data engineering team focused on innovation.
- Benefits: Remote work, flexible hours, and potential for permanent position.
- Other info: 3-month contract with excellent growth opportunities.
- Why this job: Make a real impact with cutting-edge AWS technologies.
- Qualifications: Experience with Python, PySpark, and AWS tools required.
The predicted salary is between 54000 - 66000 £ per year.
We are looking for a DevOps Engineer to join a data engineering team, building and supporting data pipelines that connect, transform and load large and complex data feeds into an AWS lakehouse environment.
Contract Duration: 3 months
Mode: Remote
Location: UK
Roles & Responsibility:
- Build and manage data pipelines to connect different data sources and move data into the AWS data platform.
- Create and improve pipelines for large and complex data feeds, including CDC and parallel data loads.
- Work with AWS Glue, S3, MWAA and Apache Iceberg to build reliable and scalable data solutions.
- Use Python and PySpark for data processing and Terraform for infrastructure.
- Work with Lead Data Engineers to ensure pipeline designs are correct, reusable and perform well.
- Support code reviews, follow data engineering standards and help ensure smooth handover to feed suppliers.
Apply now for an opportunity to make a real impact, with potential for the role to become permanent.
Hybrid Administrator (flexible hours) in Manchester employer: Harvey Nash
Join a dynamic global banking organisation that prioritises innovation and employee development. With a collaborative work culture, comprehensive benefits, and ample opportunities for professional growth, this role as a SWIFT / Payments Project Manager offers a chance to make a significant impact in the financial sector while enjoying a supportive environment in a thriving location.
StudySmarter Expert Advice🤫
We think this is how you could land Hybrid Administrator (flexible hours) in Manchester
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Harvey Nash!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Hybrid Administrator (flexible hours) at Harvey Nash.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Harvey Nash.
✨Apply Directly through Our Website
When you find a suitable opening like Hybrid Administrator (flexible hours) at Harvey Nash, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Hybrid Administrator (flexible hours) in Manchester
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Harvey Nash, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Harvey Nash. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Harvey Nash
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Harvey Nash!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
