At a Glance
- Tasks: Build and operate data pipelines, improving data usage across GAIA’s ecosystem.
- Company: Join a forward-thinking company focused on data innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement potential.
- Why this job: Make a real impact by enhancing data capabilities and collaborating with diverse teams.
- Qualifications: Proficient in Python and SQL, with hands-on data pipeline experience.
The predicted salary is between 50000 - 70000 £ per year.
Requirements:
- Comfortable writing production-grade Python and SQL
- Hands-on data pipeline experience
- Comfortable working in the cloud with infrastructure as code
- Familiarity with modern data tooling across warehousing, transformation and business intelligence
- Collaboration skills to work with a wide range of stakeholders e.g. engineers, business stakeholders and data scientists
- Data product mindset
- Nice-to-have: Experience working with streaming or near-real-time pipelines
- Nice-to-have: Experience using LLMs to help non-technical users self-serve analytics
What the job involves:
- This is a broad and hands-on role with the chance to improve how Field uses data within and beyond the realms of GAIA’s ecosystem
- Building and operating the production data pipelines and streaming integrations that ingest a wide range from market data to asset telemetry
- Partnering with our trading team to deliver the required data and capabilities for in-house forecasting model development
- Guiding and partnering with stakeholders in the wider business to build out integrations and analytical views over their data
Software Engineer (Data) in London employer: FIELD
As a Software Engineer (Data) at our company, you will thrive in a dynamic and collaborative work culture that values innovation and continuous learning. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages you to take ownership of your projects. Located in a vibrant area, our team enjoys a unique blend of flexibility and engagement, making it an excellent place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer (Data) 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 FIELD!
✨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 Software Engineer (Data) at FIELD.
✨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 FIELD.
✨Apply Directly through Our Website
When you find a suitable opening like Software Engineer (Data) at FIELD, 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 Software Engineer (Data) 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 FIELD, 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 FIELD. 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 FIELD
✨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 FIELD!
✨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.
