At a Glance
- Tasks: Build and optimise data pipelines using AWS technologies for analytics and machine learning.
- Company: Join a forward-thinking tech company focused on data innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge technology and career advancement.
- Why this job: Make an impact by developing scalable solutions that drive data-driven decisions.
- Qualifications: Strong experience in AWS data engineering and proficiency in Python.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Strong experience in data engineering within AWS cloud environments
- Hands-on experience with AWS big data technologies such as EMR, S3 and SageMaker
- Proficiency in Python for building scalable data pipelines and processing frameworks
- Experience with Apache Spark for distributed data processing
- Experience designing and maintaining scalable batch and real-time data pipelines
- Solid understanding of ETL/ELT design patterns and data modelling techniques
- Experience with workflow orchestration tools such as Apache Airflow (ideally deployed on AWS)
- Familiarity with containerisation and orchestration using Docker and Kubernetes (EKS)
- Experience with infrastructure as code (e.g. Terraform) and CI/CD/GitOps practices
- Proven ability to optimise performance and reduce cloud costs through partitioning, clustering and workload management
- Understanding of data security principles, including data loss prevention (DLP)
- (Desirable) Experience with Databricks or similar third-party big data platforms
- (Desirable) Knowledge of real-time streaming technologies (e.g. Kafka, Kinesis)
- (Desirable) Experience implementing data governance and compliance frameworks
- (Desirable) Familiarity with monitoring and observability tools in AWS environments
- (Desirable) Exposure to Lakehouse or modern data platform architectures
What the job involves
We are looking for a Data Engineer to work closely with the Data Science team to develop robust data pipelines that feed analytics and machine learning tools such as Amazon SageMaker and third-party platforms like Databricks. You will leverage AWS technologies such as EMR, S3, EKS and Airflow to process and orchestrate high-volume datasets, ensuring solutions are scalable, resilient and cost-efficient. You will also play a key role in embedding data loss prevention (DLP) principles and controls into data pipelines to protect sensitive information, while ensuring data is reliable, accessible, well-governed and optimised for downstream consumption.
Senior Data Platform Engineer employer: Deepstreamtech
As a Senior Data Platform Engineer at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including flexible working arrangements and opportunities for professional development, all set in a vibrant location that fosters creativity and growth. Join us to make a meaningful impact while working with cutting-edge AWS technologies and contributing to exciting data-driven projects.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Platform Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Senior Data Platform Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS technologies like EMR and SageMaker. We want to see how you’ve tackled real-world problems with data pipelines and processing frameworks.
✨Tip Number 3
Prepare for the interview by brushing up on your knowledge of ETL/ELT design patterns and data modelling techniques. We recommend practising common interview questions related to Apache Spark and workflow orchestration tools like Airflow.
✨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 Senior Data Platform Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with AWS big data technologies and Python. We want to see how your skills match the job description, so don’t be shy about showcasing your relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you’re the perfect fit for the Senior Data Platform Engineer role. Share specific examples of your work with data pipelines and any experience with tools like Apache Airflow or Docker.
Showcase Your Problem-Solving Skills:In your application, mention instances where you’ve optimised performance or reduced costs in cloud environments. We love seeing how you tackle challenges, especially when it comes to data engineering!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Deepstreamtech
✨Know Your AWS Inside Out
Make sure you brush up on your AWS knowledge, especially around EMR, S3, and SageMaker. Be ready to discuss how you've used these technologies in past projects, as well as any challenges you faced and how you overcame them.
✨Show Off Your Python Skills
Prepare to demonstrate your proficiency in Python by discussing specific examples of scalable data pipelines you've built. If possible, bring along code snippets or projects that showcase your ability to process data efficiently.
✨Get Familiar with ETL/ELT Patterns
Understand the key ETL/ELT design patterns and be prepared to explain how you've applied them in your work. This will show that you not only know the theory but can also implement it effectively in real-world scenarios.
✨Discuss Data Security Principles
Since data loss prevention is crucial for this role, be ready to talk about your understanding of data security principles. Share any experiences where you implemented DLP controls in your data pipelines to protect sensitive information.
