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- Tasks: Join a dynamic team to maintain and expand data pipelines using cutting-edge tools.
- Company: Harrington Starr partners with a leading payments business, driving innovation in the financial sector.
- Benefits: Enjoy hybrid/remote work flexibility and competitive salary of £95,000.
- Why this job: Be part of an exciting growth journey, collaborating with quant teams and external clients.
- Qualifications: Experience in Data Engineering, Python, AWS, and data warehousing tools required.
- Other info: Ideal for those passionate about data in the fast-paced financial services industry.
The predicted salary is between 78000 - 112000 £ per year.
Harrington Starr is currently working with a leading payments business that is looking to hire Data Engineers to help them further grow their already exciting business.
Within this role, you will be responsible for maintaining, supporting and expanding existing data pipelines using DBT, Snowflake and S3. You will also be tasked with implementing standardised data ingress/egress pipelines coupled with onboarding new, disparate data sets, sourced from many and varied data vendors, covering all asset types and frequencies from daily to real-time.
To be successful in this role you must be able to work and build relationships with internal teams as you will be working with the quant teams to streamline the data to make it easier to use on the platform. You will also be working with external clients to help them onboard their data to the live platform.
Key Skills:- Experience in Data Engineering, including data integration, modelling, optimisation and data quality
- Exceptional understanding of Python
- Experience developing in the cloud (AWS preferred)
- Solid understanding of libraries like Pandas and NumPy
- Experience in data warehousing tools like Snowflake, Databricks, BigQuery
- Familiar with AWS Step Functions, Airflow, Dagster, or other workflow orchestration tools
- Commercial experience with performant database programming in SQL
- Capability to solve complex technical issues, comprehending risks prior to the circumstance
- Comfortable working in an agile environment, where features may change and evolve quickly
- Prior experience in financial services, specifically capital markets or funds industry
If this sounds of interest please send your CV to james.platt@harringtonstarr.com
AWS Data Engineer - Hybrid/Remote employer: Harrington Starr
Contact Detail:
Harrington Starr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AWS Data Engineer - Hybrid/Remote
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, such as DBT, Snowflake, and AWS. Having hands-on experience or projects showcasing your skills with these technologies can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the data engineering field, especially those who work in financial services. Engaging with industry-specific groups on platforms like LinkedIn can help you gain insights and potentially get referrals.
✨Tip Number 3
Prepare to discuss your experience with data integration and optimisation during interviews. Be ready to share specific examples of how you've solved complex technical issues in previous roles, particularly in an agile environment.
✨Tip Number 4
Showcase your ability to collaborate with both internal teams and external clients. Highlight any past experiences where you successfully onboarded data or streamlined processes, as this will demonstrate your fit for the role.
We think you need these skills to ace AWS Data Engineer - Hybrid/Remote
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in Data Engineering, particularly with data integration, modelling, and optimisation. Emphasise your skills in Python and any cloud development experience, especially with AWS.
Craft a Compelling Cover Letter: Write a cover letter that showcases your understanding of the role and the company. Mention specific projects or experiences that demonstrate your ability to maintain and expand data pipelines using tools like DBT, Snowflake, and S3.
Highlight Key Skills: In your application, clearly outline your proficiency with libraries such as Pandas and NumPy, as well as your experience with data warehousing tools. Make sure to mention any familiarity with workflow orchestration tools like AWS Step Functions or Airflow.
Showcase Problem-Solving Abilities: Provide examples in your application that illustrate your capability to solve complex technical issues and your experience working in agile environments. This will demonstrate your readiness for the dynamic nature of the role.
How to prepare for a job interview at Harrington Starr
✨Showcase Your Technical Skills
Be prepared to discuss your experience with data engineering tools and technologies, especially DBT, Snowflake, and AWS. Highlight specific projects where you've successfully implemented data pipelines or optimised data processes.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your ability to solve complex technical issues. Prepare examples of challenges you've faced in previous roles and how you approached them, particularly in relation to data integration and quality.
✨Emphasise Collaboration
Since the role involves working closely with quant teams and external clients, be ready to discuss your experience in building relationships and collaborating effectively. Share examples of how you've worked in cross-functional teams to achieve common goals.
✨Understand the Financial Services Context
Familiarise yourself with the financial services industry, particularly capital markets or funds. Be prepared to discuss how your data engineering skills can add value in this context, and show your understanding of the specific challenges faced in this sector.
