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- Tasks: Shape messy data into reliable datasets for AI-driven products.
- Company: Fast-growing AI SaaS platform with a collaborative culture.
- Benefits: Competitive salary, hybrid working, and opportunities for mentorship.
- Why this job: Make a real impact on data foundations that power innovative AI solutions.
- Qualifications: Strong experience in Python, SQL, and cloud environments.
- Other info: Join a supportive team that values diverse perspectives and career growth.
The predicted salary is between 60000 - 85000 £ per year.
Location: London with hybrid working Monday to Wednesday in the office
Salary: £70,000 to £85,000 depending on experience
We are working with a fast growing AI driven SaaS Platform Company where data underpins intelligent products used at scale. Large volumes of first party data flow through the platform and are shaped into trusted inputs for analytics, product features, and machine learning.
They are looking for a Senior Data Engineer to take ownership of the core data pipelines that make this platform work in practice. This role is about working with real world data that directly feeds AI driven products. Data arrives incomplete, inconsistent, and sometimes ambiguous. Your role is to shape it into clear, reliable, decision ready datasets that product, analytics, and machine learning teams can confidently rely on.
Python and SQL are fundamental to the work. You will design and maintain transformation logic, think carefully about data quality and edge cases, and ensure downstream behaviour remains reliable as data volumes and AI capabilities grow. The work sits within a modern cloud data stack. You will work with technologies such as AWS, GCP or Azure, distributed processing frameworks like Spark, and modern data stores and warehouses such as Snowflake or BigQuery. The emphasis is on building robust, well structured data logic that scales alongside the platform.
You will be part of a collaborative and supportive engineering team where quality, clarity, and long term thinking are valued. Different perspectives are encouraged, and engineers are trusted to contribute in their own way. Alongside the hands on work, you will mentor and support other data engineers through code reviews, shared problem solving, and thoughtful technical guidance.
What we are looking for:
- Strong, hands on experience using Python and SQL in production environments
- Experience building and maintaining data pipelines using technologies such as Spark, Snowflake, BigQuery, or similar
- Experience working in cloud environments such as AWS, GCP or Azure, with an understanding of data processing at scale
- Confidence working with messy, real world data and improving it through careful transformation and validation
- A track record of taking ownership of important systems and seeing work through from design to long term support
- A collaborative approach with experience mentoring and supporting other engineers through code review, shared problem solving, and knowledge sharing
If this role aligns with your experience using Python and SQL and you enjoy building the data foundations that power AI driven products while supporting other engineers to do their best work, we would like to hear from you.
Right to work in the UK is required. Sponsorship is not available now or in the future.
Apply to find out more!
If you have a friend or colleague who may be interested, referrals are welcome. For each successful placement, you will be eligible for our general gift or voucher scheme.
Senior Data Engineer - Python and SQL employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer - Python and SQL
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works in data engineering. Building relationships can lead to job opportunities that aren’t even advertised.
✨Show Off Your Skills
Don’t just talk about your experience; demonstrate it! Create a portfolio showcasing your projects, especially those involving Python and SQL. Share your GitHub link when you apply through our website to give potential employers a taste of what you can do.
✨Ace the Interview
Prepare for technical interviews by brushing up on your Python and SQL skills. Practice common data engineering problems and be ready to discuss how you've tackled messy data in the past. Confidence is key, so show them you know your stuff!
✨Follow Up
After an interview, don’t forget to send a thank-you email! It’s a great way to express your appreciation and reiterate your interest in the role. Plus, it keeps you fresh in their minds as they make their decision.
We think you need these skills to ace Senior Data Engineer - Python and SQL
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and SQL, especially in production environments. We want to see how you've tackled real-world data challenges, so don’t hold back on those examples!
Showcase Your Projects: Include specific projects where you've built and maintained data pipelines using tools like Spark or Snowflake. We love seeing hands-on experience, so share the details of what you did and the impact it had!
Be Clear and Concise: When writing your cover letter, keep it clear and to the point. We appreciate a straightforward approach that outlines why you're a great fit for the role and how you can contribute to our team.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Datatech Analytics
✨Know Your Tech Stack
Make sure you’re well-versed in Python, SQL, and the cloud technologies mentioned in the job description. Brush up on your experience with AWS, GCP, or Azure, and be ready to discuss how you've used these tools to build and maintain data pipelines.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled messy, real-world data in the past. Think about specific challenges you faced and how you transformed incomplete or inconsistent datasets into reliable outputs. This will demonstrate your hands-on experience and ability to think critically.
✨Emphasise Collaboration
Since the role values a collaborative approach, be ready to talk about your experiences mentoring other engineers. Share instances where you’ve contributed to code reviews or helped solve problems as a team. This shows you’re not just a lone wolf but someone who thrives in a supportive environment.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s data practices and the engineering team’s dynamics. Inquire about their current challenges with data quality or how they envision the growth of their AI capabilities. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
