At a Glance
- Tasks: Build and manage data pipelines using AWS and advanced data management tools.
- Company: Join a forward-thinking tech company focused on data-driven solutions.
- Benefits: Enjoy remote work flexibility, competitive pay, and opportunities for professional growth.
- Other info: Collaborative team environment with a focus on innovation and career advancement.
- Why this job: Make an impact by leveraging cutting-edge technologies to solve real-world data challenges.
- Qualifications: Experience with Python, SQL, and data management tools is essential.
The predicted salary is between 50000 - 70000 £ per year.
Contract: 6 Months
Location: Remote, UK
Skills:
- Advanced knowledge of data management tools including SQL/DBMS, MongoDB, Hadoop and/or other big data technologies.
- Advanced programming skills in Java, Python, R, C++, C#, etc.
- Knowledge of statistical and data mining techniques (regression, decision trees, clustering, neural networks, etc.).
- Experience with data visualization tool is a plus.
- Exposure to online, mobile, and social data is a plus.
- Intellectual curiosity, along with excellent problem-solving and quantitative skills, including the ability to disaggregate issues, identify root causes and recommend solutions.
- Ability to independently own and drive model development, balancing demands and deadlines.
- Strong people skills, team-orientation, and a professional attitude.
Our Advanced Analytics teams bring the latest analytical techniques plus a deep understanding of industry dynamics and corporate functions to help clients create the most value from data.
Must Have:
- Enterprise Knowledge Data Management, Data pipeline, Python, SQL, AWS.
- Centralising data stored in RDBMS, Data Ingestion, and Uploading data into the database.
Education:
Bachelor's degree in quantitative field like Computer Science, Engineering, Statistics, Mathematics or related field required. Advanced degree is a strong plus.
StudySmarter Expert Advice🤫
We think this is how you could land Remote AWS Data Engineer in Halifax
✨Get Involved in Data Science Meetups
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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 Grabjobs.
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We think you need these skills to ace Remote AWS Data Engineer in Halifax
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 Grabjobs, 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 Grabjobs. 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 Grabjobs
✨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 Grabjobs!
✨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.
