At a Glance
- Tasks: Ingest data into AWS data lake and build ELT pipelines using dbt and Athena.
- Company: Join Blackstone&, a leader in government data programmes.
- Benefits: Flexible working, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on data quality and reliability.
- Why this job: Make a difference in public sector data while working with cutting-edge technologies.
- Qualifications: Experience with Python, Airflow, Terraform, and CI/CD is essential.
The predicted salary is between 63000 - 77000 £ per year.
Blackstone& is seeking a hands-on Data Engineer to join a UK government data programme.
You will ingest data into an AWS data lake, build ELT pipelines with dbt and Athena, and ensure data quality for analysts and data scientists.
You will work with analytics engineers, data scientists and source-system teams, using Python, Airflow, Terraform and CI/CD to deliver reliable data products in a hybrid on-site/off-site setup.
Data Engineer: AWS Data Lake for Public Sector in England employer: Blackstone& London
At Blackstone London, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through mentorship opportunities and a focus on professional development, all within a supportive hybrid work environment. Join us to make a meaningful impact in the healthcare sector while enjoying the benefits of a forward-thinking company that values your contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer: AWS Data Lake for Public Sector in England
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We think you need these skills to ace Data Engineer: AWS Data Lake for Public Sector in England
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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✨Brush Up on Your Statistics
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