At a Glance
- Tasks: Build scalable data pipelines and automate workflows using cutting-edge tools.
- Company: Join a high-impact engineering team in a collaborative environment.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Exciting opportunity to start in August with excellent career development prospects.
- Why this job: Work with advanced technologies and make a real impact on marketing insights.
- Qualifications: Experience in cloud environments and strong skills in AWS, PySpark, and Databricks.
Location: This is a hybrid engagement represented by 2 days/week onsite, either in Central London or Glasgow.
Start Date: Must be able to start mid-August.
Salary: £80k-£90k (Senior) | £90k-£95k (Lead)
About The Role
Our partner is looking for a Senior Data Engineer to join a high-impact engineering team delivering scalable data solutions for complex marketing and customer insight use cases. This is an opportunity to work on cutting-edge data pipelines, cloud-native platforms and real-time data flows in a collaborative, forward-thinking environment. You’ll be involved in designing and building production-grade ETL pipelines, driving DevOps practices across data systems and contributing to high-availability architectures using tools like Databricks, Spark and Airflow - all within a modern AWS ecosystem.
Responsibilities
- Architect and build scalable, secure data pipelines using AWS, Databricks and PySpark.
- Design and implement robust ETL/ELT solutions for both structured and unstructured data.
- Automate workflows and orchestrate jobs using Airflow and GitHub Actions.
- Integrate data with third-party APIs to support real-time marketing insights.
- Collaborate closely with cross-functional teams including Data Science, Software Engineering and Product.
- Champion best practices in data governance, observability and compliance.
- Contribute to CI/CD pipeline development and infrastructure automation (Terraform, AWS DevOps).
- Provide input into technical decisions, peer reviews and solution design.
Requirements
- Proven experience as a Data Engineer in cloud-first environments.
- Strong commercial knowledge of AWS services (e.g. S3, Glue, Redshift).
- Advanced PySpark and Databricks experience (Delta Lake, Unity Catalog, Databricks Jobs etc).
- Proficient in SQL (T-SQL/SparkSQL) and Python for data transformation and scripting.
- Hands-on experience with workflow orchestration tools such as Airflow.
- Strong version control and DevOps exposure (Git, GitHub Actions, Terraform).
- Familiar with data quality tools and metadata/cataloguing (e.g. Great Expectations, Unity Catalog).
Beneficial: MarTech domain knowledge.
Remote Senior Data Engineer - AWS/Databricks/PySpark - August Start Date employer: WüNDER_TALENT
Join a dynamic and innovative team as a Senior Data Engineer, where you'll have the opportunity to work on cutting-edge data solutions in a hybrid environment that promotes collaboration and flexibility. With a strong focus on employee growth, our company offers extensive training and development opportunities, alongside a competitive salary and benefits package. Experience a vibrant work culture in either Central London or Glasgow, where your contributions will directly impact marketing insights and customer engagement.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Senior Data Engineer - AWS/Databricks/PySpark - August Start Date
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with AWS, Databricks, or PySpark. A friendly chat can lead to insider info about job openings or even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ETL pipelines and cloud-native solutions. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common data engineering scenarios and be ready to discuss how you've tackled challenges in past projects.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Remote Senior Data Engineer - AWS/Databricks/PySpark - August Start Date
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with AWS, Databricks, and PySpark, and don’t forget to showcase any relevant projects that demonstrate your skills in building scalable data pipelines.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your background aligns with our needs. Mention specific tools and technologies you’ve worked with that are relevant to the role.
Showcase Your Projects:If you’ve worked on any interesting data projects, make sure to include them in your application. Whether it’s a personal project or something from your previous job, we love seeing real-world applications of your skills!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status directly!
How to prepare for a job interview at WüNDER_TALENT
✨Know Your Tech Stack
Make sure you brush up on your AWS, Databricks, and PySpark knowledge. Be ready to discuss specific projects where you've used these technologies, as well as any challenges you faced and how you overcame them.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've designed and built ETL pipelines in the past. Think of examples that highlight your ability to handle both structured and unstructured data, and be ready to explain your thought process during those projects.
✨Collaboration is Key
Since this role involves working closely with cross-functional teams, be prepared to share experiences where you've collaborated with Data Science or Software Engineering teams. Highlight how you communicated technical concepts to non-technical stakeholders.
✨Emphasise Best Practices
Familiarise yourself with data governance and compliance best practices. Be ready to discuss how you've implemented these in previous roles, especially in relation to CI/CD pipeline development and infrastructure automation.
