At a Glance
- Tasks: Design and maintain scalable data pipelines using SQL and Python, leading a team of data engineers.
- Company: Join Artefact, a fast-growing data service provider transforming businesses with data and AI.
- Benefits: Hybrid work, continuous learning opportunities, and a chance to make a real impact.
- Other info: Be part of a diverse team that values innovation and ownership.
- Why this job: Work on cutting-edge projects with modern technologies and develop your skills in a dynamic environment.
- Qualifications: 3+ years in data engineering, strong SQL and Python skills, and leadership experience.
The predicted salary is between 63000 - 77000 £ per year.
Описание
Artefact is a new generation of data service provider, specialising in data consulting and data-driven digital marketing, dedicated to transforming data into business impact across the entire value chain of organisations.
We are proud to say we’re enjoying skyrocketing growth.
Задачи
- Design, build, and maintain scalable and robust data pipelines using SQL and Python, and leveraging technologies such as: Databricks, Snowflake, Azure Data Factory, AWS Glue, Apache Airflow and Pyspark.
- Lead the integration of complex data systems and ensure consistency and accuracy of data across multiple platforms.
- Implement continuous integration and continuous deployment (CI/CD) practices for data pipelines to improve efficiency and quality of data processing.
- Work closely with data architects, analysts, and other stakeholders to understand business requirements and translate them into technical implementations.
- Oversee and manage a team of data engineers, providing guidance and mentorship to ensure high-quality project deliverables.
- Knowledge of best practices in data governance, security, and compliance within the organisation.
- Continuously evaluate new technologies, AI trends and tools to enhance the capabilities of the data engineering function.
Требования
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
- 3+ years of industry experience in data engineering with a strong technical proficiency in SQL, Python, and big data technologies.
- Expertise in building scalable data workflows using cloud-native orchestration tools and distributed data processing frameworks.
- Solid understanding of CI/CD principles and Dev Ops/Dev Sec Ops practices.
- Experience leading on project workstreams.
- Excellent problem-solving skills and the ability to work with ambiguity.
- Proficient in leveraging AI-assisted workflows to optimise task efficiency.
- Strong communication and interpersonal skills.
Будет плюсом
- Certifications in Azure, AWS, or GCP;
- Certifications in Databricks, Snowflake or similar technologies;
- Experience in the leading large scale data engineering projects;
- Excellent understanding of data architecture involving data mesh, data lake, data warehouse and data lakehouse;
- Demonstrated experience with Infrastructure as Code tooling such as Terraform
Условия
Join one of the fastest-growing global data consulting companies and help leading organisations transform through data and AI.
Work on cutting-edge projects with modern technologies including AI, Machine Learning, Cloud, and Dev Ops, while continuously developing your technical, consulting, and leadership skills.
Be part of a diverse, international, and action-oriented team of experts across data, consulting, engineering, and digital marketing.
We value initiative, innovation, and ownership.
Your ideas matter, and you’ll have the opportunity to make a real impact from day one.
- Hybrid work arrangement: two days per week working from the office
Data engineer in AI in London employer: Artefact
Artefact is an exceptional employer, offering a dynamic hybrid working environment that fosters collaboration between our London and Cornwall offices. With a strong focus on employee growth, we provide extensive learning opportunities, generous annual leave, and unique benefits like a birthday day off and subsidised gym memberships, all while being part of a diverse team dedicated to transforming data into impactful business solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Data engineer in AI in London
✨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 Artefact!
✨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 Data engineer in AI at Artefact.
✨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 Artefact.
✨Apply Directly through Our Website
When you find a suitable opening like Data engineer in AI at Artefact, 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 Data engineer in AI in London
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 Artefact, 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 Artefact. 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 Artefact
✨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 Artefact!
✨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.
