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- Tasks: Join us as a Data Engineer to build and optimize data pipelines for finance analytics.
- Company: Be part of a dynamic team focused on innovative data solutions in finance technology.
- Benefits: Enjoy a hybrid work model with three days in the office and collaborative team culture.
- Why this job: Contribute to impactful projects while working with cutting-edge technologies like Snowflake and Azure.
- Qualifications: Expertise in Python, Snowflake, and data engineering; familiarity with finance data is a plus.
- Other info: Engage in agile development cycles and collaborate with cross-functional teams.
The predicted salary is between 43200 - 72000 £ per year.
Data Engineer – Finance Analytics Technology
We are looking for a dedicated Data Engineer to join the Finance Analytics Technology team. In this role, you’ll play a key part in building, maintaining, and optimising a modern data ecosystem. This permanent opportunity involves working with leading technologies, including Snowflake, Python, Informatica, and Azure, to deliver high-quality data solutions that support business-critical decision-making.
With a hybrid working model and three days a week in the office, this role provides the chance to collaborate closely with cross-functional teams in a dynamic and supportive environment.]
Key Responsibilities:
- Design, build, and optimise scalable data pipelines using ETL and ELT methodologies.
- Utilise Snowflake for efficient data storage, processing, and analytics.
- Automate data processes and integrate data from multiple sources using Python and SQL.
- Leverage Azure cloud-native technologies to enhance data infrastructure, ensuring scalability, performance, and security.
- Collaborate with data analysts, BI developers, and enterprise data teams to align solutions with business requirements and maintain data governance standards.
- Apply domain knowledge in finance-related data to improve accuracy, enhance models, and meet business needs.
- Stay informed about developments in cloud and data technologies, contributing to the organisation’s data strategy.
- Participate fully in the agile development lifecycle, including sprint planning, design reviews, and delivering data tasks within two-week cycles.
- Ensure compliance with existing standards while contributing to the refinement of best practices in cloud data engineering.
Essential Skills
- Expertise in building data pipelines and architectures with Snowflake, Python, and Informatica.
- Familiarity with Azure and other cloud-native technologies.
- Strong understanding of finance-related data domains and their application in data engineering.
- Problem-solving ability, combined with excellent collaboration and communication skills, to work effectively with technical and non-technical teams.
- Experience working within modern technology stacks and agile methodologies.
- Background in collaborating with geographically distributed development teams.
Desirable Skills
- Knowledge of reporting tools such as Power BI.
- Familiarity with SAP FI datasets or platforms like SAP BW, SAP Analysis, and Business Objects.
This is an exciting opportunity to contribute to meaningful data-driven initiatives, working with a forward-thinking team on innovative projects. If this sounds like your next step, we’d love to hear from you!
Finance Data Engineer employer: Qh4 Consulting
Contact Detail:
Qh4 Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Finance Data Engineer
✨Tip Number 1
Familiarize yourself with the specific technologies mentioned in the job description, such as Snowflake, Python, and Azure. Consider building a small project or contributing to an open-source project that utilizes these tools to demonstrate your hands-on experience.
✨Tip Number 2
Network with professionals in the finance data engineering field. Attend relevant meetups or webinars where you can connect with others who work with similar technologies and may have insights into the hiring process at companies like us.
✨Tip Number 3
Stay updated on the latest trends in cloud and data technologies. Follow industry blogs, join forums, or participate in online courses to enhance your knowledge and show your commitment to continuous learning during interviews.
✨Tip Number 4
Prepare to discuss your experience with agile methodologies and how you've collaborated with cross-functional teams in the past. Be ready to share specific examples that highlight your problem-solving skills and ability to work effectively in a team environment.
We think you need these skills to ace Finance Data Engineer
Some tips for your application 🫡
Understand the Role: Take the time to thoroughly read the job description for the Finance Data Engineer position. Make sure you understand the key responsibilities and essential skills required, as this will help you tailor your application.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience with Snowflake, Python, Informatica, and Azure. Provide specific examples of projects where you built data pipelines or worked with finance-related data to demonstrate your expertise.
Showcase Collaboration Skills: Since the role involves working closely with cross-functional teams, highlight your collaboration and communication skills. Mention any experience you have in agile environments or working with geographically distributed teams.
Tailor Your Application: Customize your cover letter to reflect your passion for data engineering and how your background aligns with the company's goals. Mention your interest in contributing to data-driven initiatives and staying updated on cloud and data technologies.
How to prepare for a job interview at Qh4 Consulting
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Snowflake, Python, and Informatica in detail. Highlight specific projects where you built data pipelines or optimized data processes, as this will demonstrate your hands-on expertise.
✨Understand the Finance Domain
Since the role involves finance-related data, brush up on key concepts in finance that relate to data engineering. Be ready to explain how your knowledge can enhance data accuracy and model performance.
✨Emphasize Collaboration Experience
This position requires working closely with cross-functional teams. Share examples of how you've successfully collaborated with both technical and non-technical teams, especially in agile environments.
✨Stay Updated on Technology Trends
Demonstrate your commitment to continuous learning by discussing recent developments in cloud and data technologies. Mention any relevant courses or certifications you've completed to show your proactive approach.
