At a Glance
- Tasks: Build secure data pipelines and transform data for impactful analytics.
- Company: Renowned IT client offering end-to-end solutions in the UK and EU.
- Benefits: Remote work, competitive pay, and opportunities for professional growth.
- Other info: Contract role with potential for exciting projects in geospatial data.
- Why this job: Join a dynamic team and make a difference with advanced data technologies.
- Qualifications: Experience in SQL, Python, and data transformation required.
The predicted salary is between 36000 - 60000 £ per year.
We provide end-to-end IT solutions and services including Applications services, Data & Analytics services, AI/ML Technologies and Professional services in the UK and EU market.
We are looking to hire a Data Engineer for an Advanced Analytics role for one of our renowned IT clients in the UK. This is a contract role and remote working.
A strong Data Engineer is required to work on the Data Transformation workstream within the client’s Digital Screening. The core focus of this role is to build secure, repeatable data ingestion and transformation pipelines, to implement data cleansing rules, and to produce auditable, reproducible outputs.
- Capability in data transformation-heavy pipelines from data profiling to cleansing to standardization, conformance and publishing.
- Advanced knowledge of SQL for profiling, joins/merges, deduplication, anomaly detection, and performance tuning.
- Scripting knowledge in Python for automation, parsing, rules engines, and data quality checks, with the ability to write maintainable code.
- Experience using Python packages for data wrangling (e.g. Pandas, Polars), modelling (e.g. scikit-learn) and visualization (e.g. matplotlib).
- Experience with modern data tooling (for example, Spark, Azure Data Factory) or the ability to implement equivalents with code.
- Proven experience working with geospatial data, including handling spatial formats (e.g., vector, raster, GeoJSON, shapefiles), coordinate reference systems, and spatial analysis workflows.
- Strong ability to interpret and apply geographical context in data processing pipelines, with demonstrated capability to aggregate, upscale, or translate local/regional geospatial insights into broader national or regional-level datasets and analytical outputs.
- Experience working with publicly available official datasets, particularly Office for National Statistics (ONS) open data products (e.g., census boundaries, geographic lookups, deprivation indices, or mid-year population estimates).
- Able to build rules for completeness, validity, and consistency, and implement exception handling.
Data Engineer employer: Technopride Ltd
As an AWS Architect at our London-based company, you will thrive in a dynamic and flexible work environment that champions innovation and collaboration. We offer competitive benefits, a strong focus on employee growth through continuous learning opportunities, and a culture that values diversity and teamwork. Join us to be part of a forward-thinking team dedicated to building cutting-edge cloud solutions while enjoying the perks of hybrid working.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Networking is key! Reach out to professionals in the data engineering field on LinkedIn or attend local meetups. We can leverage our connections to get insights and maybe even referrals for that Data Engineer role.
✨Tip Number 2
Prepare for technical interviews by brushing up on your SQL and Python skills. We recommend doing mock interviews with friends or using online platforms to simulate the real deal. Practice makes perfect!
✨Tip Number 3
Showcase your projects! If you've worked on data transformation pipelines or geospatial data, make sure to highlight these in your discussions. We want to see your hands-on experience and how you tackle real-world problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to ensure your application gets noticed. Plus, we often have exclusive roles listed there that you won’t find anywhere else.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with SQL, Python, and any data transformation projects you've worked on. We want to see how your skills match what we're looking for!
Showcase Your Projects:Include specific examples of projects where you've built data pipelines or worked with geospatial data. This helps us understand your hands-on experience and how you tackle real-world problems.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get the best chance to shine in front of our hiring team!
How to prepare for a job interview at Technopride Ltd
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of data transformation and the tools mentioned in the job description. Brush up on your SQL skills, especially around profiling and performance tuning, and be ready to discuss how you've used Python for data wrangling and automation.
✨Showcase Your Geospatial Knowledge
Since the role involves working with geospatial data, prepare examples of past projects where you handled spatial formats or conducted spatial analysis. Be ready to explain how you interpreted geographical context in your data processing pipelines.
✨Prepare for Technical Questions
Expect technical questions that dive deep into your experience with data pipelines and cleansing rules. Practice explaining your thought process and the steps you take when building secure and repeatable data ingestion processes.
✨Demonstrate Problem-Solving Skills
Be prepared to discuss how you handle exceptions and ensure data validity and consistency. Think of specific scenarios where you’ve implemented exception handling and how it improved your data quality checks.
