At a Glance
- Tasks: Design and build scalable data solutions to support business needs.
- Company: Join a leading financial services firm focused on data innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous learning and development.
- Why this job: Make an impact by modernising data platforms and driving efficiency.
- Qualifications: Experience in data engineering and knowledge of modern data concepts required.
The predicted salary is between 63000 - 77000 £ per year.
Purpose
Design, build, test and release data solutions to support the requirements of the business.
Working within the data engineering team, you will deliver robust, scalable and secure data pipelines and contribute to the modernisation of 7IM’s data platform, tools and processes.
Responsibilities
- Build robust and scalable data solutions that meet technical and security standards
- Contribute to the design of data solutions in line with the architecture and roadmap
- Design, develop, test and release robust, scalable software solutions using modern technologies and best practices
- Develop and maintain data models, pipelines and documentation
- Adhere to engineering standards and help keep technical debt in check
- Collaborate with business stakeholders and technical teams to analyse needs, propose solutions and ensure alignment
- Contribute to initiatives that modernise technology, tools and development processes
- Peer review code and documentation for accuracy, maintainability and supportability
- Provide second-line support and guidance to application support teams
- Identify opportunities for technical efficiencies and share best practice
- Ensure compliance with regulatory standards and internal policies
- Other duties as reasonably required by line management and 7IM
About You
Experience
- Experience in a data engineering role, ideally within a regulated environment
• Knowledge of the following is required
- Modern Data platform concepts; Data Lake, Lakehouse, Data Warehouse, Data Vault
- ETL / ELT processes and designing, building and testing data pipelines
- Building data transformations using Python /Py Spark
- Azure Cloud Version control and CI/CD tools, specifically Azure Dev Ops Service
- Analytics and MI products including MS Power BI
- Data catalogue & governance using MS Purview
• Knowledge of the following would be desirable
- Microsoft server-based data products (SQL Server, Analysis Services, Integration Services and Reporting Services)
- Enterprise Architecture tools (e. g. Lean IX, Ardoq), Frameworks (TOGAF) and core artefacts (Capability Models, Technical Reference Models, Data Flow Diagrams)
- Experience developing and implementing technology roadmaps and target state architectures
- Experience integrating bespoke software, commercial off-the-shelf packages and third-party services
- Demonstrable experience of migrating on-premises workloads to cloud-native services
- Excellent analytical, problem-solving and communication skills
- Strong stakeholder management and influencing abilities at all levels
- Commitment to continuous learning and keeping skills current
Skills
- Able to work independently within the data space and comfortable dealing with some ambiguity
- Comfortable delivering to plan in a fast-paced environment
- Excellent verbal and written communication with a proven track record of stakeholder engagement and influencing both business and technical stakeholders
- Ability to communicate between the technical and non-technical - interpreting the needs of technical and business stakeholders, communicating how activities meet strategic goals and client needs
- Ability to analyse data to drive efficiency and optimisation, design processes and tools to monitor production systems and data accuracy
- Ability to produce, compare, and align different data models across multiple subject areas, reverse-engineering data models from a live system where required
- Excellent analytical and numerical skills are essential, enabling easy interpretation and analysis of large volumes of data
- Excellent problem-solving and data modelling skills (logical, physical, semantic and integration models)
Qualifications
- Relevant degree or equivalent experience.
- Azure DP-203 (or equivalent)
- Certification in Data Governance and Stewardship Professional (DGSP) or similar (desirable).
- Evidence of business experience or formal business qualifications (desirable).
- Other relevant information
- Experience of wealth management (including operational knowledge) would be advantageous
- Prior experience working in Financial Services preferred thorough understanding of data security, data privacy, and GDPR required
- #J-18808-Ljbffr
Data Engineer employer: Seven Investment Management LLP
7IM is an exceptional employer that prioritises employee growth and development, offering comprehensive support for professional qualifications while fostering a collaborative and inclusive work culture. Located in a vibrant region, our team enjoys a dynamic environment where innovation thrives, and employees are encouraged to build meaningful relationships with clients and colleagues alike. With a strong commitment to service excellence and adherence to FCA principles, 7IM provides a rewarding career path for those looking to make a significant impact in the investment sector.
Contact Details:
Seven Investment Management LLP Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
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We think you need these skills to ace Data Engineer
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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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
