At a Glance
- Tasks: Design and optimise data pipelines for financial data management using cutting-edge technologies.
- Company: Join Aiviq, a fintech innovator transforming global asset management.
- Benefits: Enjoy hybrid work, professional development, and a collaborative culture.
- Other info: Dynamic environment with opportunities for growth and learning.
- Why this job: Make a real impact on financial data integrity and business decisions.
- Qualifications: 5+ years in data engineering with strong SQL and Azure skills.
The predicted salary is between 60000 - 80000 £ per year.
Aiviq is a fintech company transforming how asset managers globally process, analyse, and leverage their data. Our solutions power critical business processes for leading financial institutions worldwide, helping them drive efficiency and growth.
We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles.
Core Responsibilities- Design, build, and optimise data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments.
- Develop and maintain Spark SQL notebooks for complex data transformations and analysis.
- Translate business logic and financial calculation requirements into clear, maintainable code.
- Create data integrity checking scripts and validation frameworks in collaboration with QA teams.
- Implement automated data quality checks and reconciliation processes.
- Assist with the maintenance of a curated, anonymised dataset for system testing that covers all known scenarios and edge cases.
- Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues.
- Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved.
- Consult on database architecture decisions, balancing performance, scalability, and maintainability.
- Optimise query performance and data processing workflows for large-scale financial datasets.
- Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies.
- Think end-to-end about data flows while ensuring rigorous attention to implementation details.
- Create and maintain comprehensive documentation of database schemas, processes, and data flows.
- Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms.
- Document data transformation logic and calculation methodologies for audit and compliance purposes.
- Contribute to data governance standards and best practices across the organisation.
- Act as first point of escalation for high-priority data issues in production environments.
- Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts.
- Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development.
- Support both Data CoE initiatives and product engineering priorities through effective stakeholder management.
- Technical Expertise
- Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics.
- Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development.
- Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake).
- Programming: Solid coding skills in SQL, Python, and/or C#.
- Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms.
- Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested.
- Domain Knowledge
- Ideally a proven track record working with complex financial data and calculations.
- Understanding of financial data structures, reconciliation processes, and audit requirements.
- Experience handling temporal data, slowly changing dimensions, and historical data management.
- Knowledge of data quality frameworks and validation methodologies.
- Professional Capabilities
- Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks.
- Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved.
- Experience with testing principles and data integrity validation.
- Ability to consult on technical architecture while maintaining pragmatic focus.
- Excellent documentation and process modelling capabilities.
- 5+ years in data engineering roles with increasing responsibility.
- Track record of delivering production data systems at scale.
- Experience working in matrix or cross-functional team structures.
- Knowledge of Azure Purview or data cataloguing solutions.
- Familiarity with Great Expectations or similar data quality frameworks.
- Familiarity with data anonymisation, masking, and synthetic data generation techniques.
- Experience with GraphQL APIs and modern application data layers.
- Exposure to modern data visualisation tools (Power BI, Tableau).
- Experience with data build tool (dbt) or similar transformation frameworks.
- Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds.
- Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum.
- Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved.
- Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge.
- Pragmatic problem-solver: Balances architectural thinking with practical implementation.
- Clear communicator: Articulates technical concepts to both specialist and generalist audiences.
- Ownership mentality: Takes responsibility for production systems and follows through on commitments.
- Thrives in a matrix organization with multiple stakeholders.
- Comfortable with ambiguity and competing priorities.
- Passionate about engineering excellence and continuous improvement.
- Values documentation and knowledge sharing.
- Responds well under pressure during production incidents.
- Professional Development.
- Exposure to enterprise-scale financial data systems handling complex calculations.
- Opportunity to shape data engineering standards across the organisation.
- Work with modern Azure cloud infrastructure and emerging technologies.
- Collaborate with skilled engineers across test automation, software development, and data teams.
- Matrix structure providing diverse learning opportunities from both CoE and product perspectives.
- Hybrid working arrangement with flexibility.
- Collaborative engineering culture valuing quality and craftsmanship.
- Investment in tools, training, and professional growth.
- Meaningful work on systems that impact financial data integrity and business decisions.
Aiviq is an equal opportunity employer and values diversity in our workforce. This role will be located in London although we are a hybrid team with frequent remote working and broader flexible working options available.
Senior Data Engineer employer: Aiviq Limited
Aiviq is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation within the fintech sector. With a strong emphasis on professional development, employees have access to cutting-edge technologies and diverse learning opportunities, all while enjoying the flexibility of a hybrid working model in the vibrant city of London. Join us to make a meaningful impact on financial data integrity and business decisions, surrounded by a team that values quality and craftsmanship.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with Aiviq employees on LinkedIn. Building relationships can open doors that job applications alone can't.
✨Tip Number 2
Show off your skills! If you have a portfolio or GitHub with projects related to data engineering, make sure to share it during interviews. It’s a great way to demonstrate your expertise and passion.
✨Tip Number 3
Prepare for technical interviews by brushing up on your SQL and Python skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨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, it shows you’re genuinely interested in joining the Aiviq team.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with MS SQL Server, Azure Synapse Analytics, and any relevant big data processing skills. We want to see how your background aligns with our needs!
Showcase Your Projects:Include specific projects where you've designed and optimised data pipelines or worked with complex financial data. This gives us a clear picture of your hands-on experience and problem-solving skills in action.
Be Clear and Concise:When writing your cover letter, be clear and concise about why you’re a great fit for the role. Use straightforward language to explain your technical expertise and how it relates to our mission at Aiviq.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Aiviq Limited
✨Know Your Tech Inside Out
Make sure you brush up on your skills with MS SQL Server, Azure Synapse Analytics, and PySpark. Be ready to discuss specific projects where you've designed and optimised data pipelines, as well as any challenges you faced and how you overcame them.
✨Showcase Your Problem-Solving Skills
Prepare to share examples of complex data issues you've tackled in the past. Highlight your analytical approach and how you dug deep to find solutions, especially in high-pressure situations. This will demonstrate your relentless problem-solving mindset.
✨Communicate Clearly and Confidently
Practice explaining technical concepts in a way that anyone can understand. You might be asked to articulate your thought process behind database architecture decisions or data integrity checks, so clarity is key. Use simple language and avoid jargon when possible.
✨Be Ready for Scenario-Based Questions
Expect questions that require you to think on your feet. For instance, you might be asked how you would handle a sudden data quality issue in production. Prepare by thinking through potential scenarios and your responses, focusing on your collaborative approach and stakeholder management.
