At a Glance
- Tasks: Join a dynamic team to innovate and prevent fraud using cutting-edge technology.
- Company: Thriving Financial Services business with a digital-first approach.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous learning and improvement.
- Why this job: Make a real impact on protecting clients across the UK & Europe.
- Qualifications: Experience in data engineering with strong Python and SQL skills.
The predicted salary is between 63000 - 77000 £ per year.
Fantastic opportunity to join a thriving Financial Services business. This is a hybrid role, and can be based out of either the London or Manchester office.
As a digital-first company, our Enterprise Security & Fraud (ES&F) team aims to innovate using cutting edge technology to prevent fraud and financial crime on our investment platform.
Join an established cybersecurity-focused development team and be on the cutting edge of implementing a suite of tools to protect hundreds of thousands of clients across the UK & Europe.
In this role you will
- Be creative with solutions for business needs, it's important that we consider our options outside of what is already being done.
We want to build resilient, future-proof novel solutions.
- Aid with the design and implement of pipelines for real-time analysis and critical decision making for operational systems.
- Build ELT pipelines, from source to presentation of data to our internal customers.
- Write high quality, testable and readable code, assist with code reviews, provide solution design input, build automated tests, create documentation, and other tasks throughout the development lifecycle.
- Collaborate closely with junior and senior developers, scrum master, and product owner to ensure the data is enabling our client experience.
- Identify and help implement continuous improvement of technical standards, methodologies, technologies, and processes.
- Own the deployment and operations of the system across environments from development, test, and through to production.
- Participate in agile meetings aligned to the scrum framework: sprint planning, daily scrums, sprint review, sprint retrospective.
Qualifications we're looking for
A bachelor's degree in computer science, STEM or related discipline is a plus, but not strictly required.
Experience as a data engineer.
Strong experience with Python and SQL is required as well as a solid understanding of working in a cloud-based environment (AWS is preferred).
Proficient using transformation tools such as Py Spark/Pandas.
Knowledge and practice of graph databases is preferred but not required.
Key responsibilities/ Skills and experience
Familiarity using data quality frameworks, ie. Great Expectations.
Experience building cloud infrastructure using Ia C (Cloud Formation/Terraform).
Demonstrable understanding of industry standards and best practices as it relates to development methodology such as testing, code quality and consistency.
Great communication skills: the ability to bridge the gap between the technical and non-technical across various communication channels.
An understanding of agile software development methodology, with scrum framework experience preferred.
A desire to continuously learn and develop yourself in both technical and non-technical skillsets.
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Senior Data Engineer - Python / PySpark / SQL in London employer: Harvey Nash Group
Join a leading Financial Services firm in London that prioritises innovation and security, offering a dynamic work culture where your expertise in information security and IT risk management will be valued. With a strong commitment to employee development, you will have access to continuous learning opportunities and the chance to lead high-performing teams in a collaborative environment. Enjoy competitive benefits and the unique advantage of working in one of the world's financial capitals, where your contributions will make a significant impact.
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We think this is how you could land Senior Data Engineer - Python / PySpark / SQL in London
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We think you need these skills to ace Senior Data Engineer - Python / PySpark / SQL 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!
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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 Group. 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 Group
✨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!
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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 Group!
✨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.
