At a Glance
- Tasks: Build data pipelines and models for our cutting-edge data platform.
- Company: Join Citywire, a leader in the global asset management industry.
- Benefits: Enjoy generous holidays, flexible working, and well-being support.
- Other info: Collaborative culture with opportunities for growth and learning.
- Why this job: Make a real impact on innovative AI systems and data solutions.
- Qualifications: Experience with dbt, Python, SQL, and AWS is essential.
The predicted salary is between 63000 - 77000 £ per year.
Citywire's data platform powers products across the company, both internal and external, from our industry ratings to our growing estate of production AI systems. We're hiring a Data Engineer to help build the pipelines and data models at the heart of it - taking requirements from stakeholder teams and turning them into tested, scheduled, monitored production systems. This is a growth role by design. You'll work directly with, and be mentored by, the platform lead - on architecture, on production engineering discipline, and on working effectively with AI coding agents at production scale.
What you'll be doing:
- Production data modelling: Translate analyst requirements and SQL into robust, production-grade dbt models (source → warehouse → reporting layers). You'll own data contracts, documentation, testing, and scheduling, with orchestration managed via Prefect.
- Large-scale fund and manager performance computation: Help decompose a legacy performance engine into a modern, event-driven AWS architecture (EventBridge, SQS, Lambda, S3, RDS). You'll build parallel compute pipelines using Spark on EMR (no deep prior Spark experience needed, just a strong curiosity for distributed systems).
- Data models for AI systems: Partner with the Platform Lead to design, build, and maintain the data models powering our AI platform - spanning fund and share class data, clickstream interactions, and CRM and finance systems.
- Alerting and observability: Ensure operational health across all pipelines via structured logging, metrics, DLQ tracking, and purposeful alerting. We view observability as a core engineering practice and will support your growth in designing resilient event-driven systems. Work directly with the analyst team and the data quality team, from requirement through deployment.
What we're looking for:
- Real dbt experience: You've built and maintained models in a genuine project - incremental logic, tests, an understanding of why downstream consumers matter. It doesn't need to have been enormous; it needs to have been real. BigQuery experience is a plus; another warehouse is fine.
- Solid Python and SQL skills: You have the instinct that code going to production deserves tests.
- Practical AWS Experience: You have hands-on experience deploying and debugging in AWS. You don't need to know every service we use on day one - we’ll teach you our specific stack.
- Strong CLI Skills: Proficient with terminal-driven workflows, containerized environments, and shell scripting for debugging, automation, and local development.
- Pragmatic AI Tooling: You leverage AI coding assistants to speed up your workflow, but bring critical oversight to their output - reviewing, testing, and verifying generated code rather than shipping it on faith.
- Data Intuition & Rigor: You look beyond passing CI/CD checks. When a pipeline runs green but the outputs look off, you dig into the numbers, comparing runs, spotting unexpected anomalies, and validating the business logic behind the data.
- Accountability & Resilience: You take pride in what you build. You learn from production mistakes, own the outcome, and prefer holding accountability for your own pipelines over handing off maintenance to someone else.
- Collaborative Communication: You can sit side-by-side with analysts, understand the intent behind their SQL, and partner with them to translate complex business logic into efficient, production-ready solutions.
Nice to Have (Genuinely learnable here - we will train you):
- Frameworks & Tools: Spark on EMR, Prefect (or experience transferring from Airflow/Dagster), OpenSearch.
- Architecture: Event-driven design patterns, SurrealDB or graph data stores.
- Domain Knowledge: Financial services or asset management context.
Meet Citywire: We cover - and connect - all sides of the $100 trillion global asset management industry - through our news, events and insights. At Citywire, we uphold a culture rooted in honesty, integrity, and fairness, where every voice is valued and heard. Our culture promotes constructive dialogue and collaboration on a global scale. Join the team at the Heart of Wealth.
Our perks:
- Generous holiday entitlement: Start with 25 days per annum, increasing to 28 days after three years' service, and 30 days after five years' service, in addition to bank holidays.
- Flexible working options.
- Annual allowance for well-being activities or gym memberships, with assistance available for monthly or annual costs.
- Eye-test and glasses allowance.
- Competitive private pension scheme.
- Critical illness cover and group life assurance from day one of employment.
- Well-being support: Access to an independent Employee Assistance Programme, available 24/7.
- Cycle to work scheme and annual travel card loans.
- Techscheme: Purchase the latest tech through our employer scheme, spreading the cost over 12 months with National Insurance savings.
- After two years of continuous service, access group income protection, private medical, and dental insurance.
Citywire is an equal opportunities employer.
Data Engineer employer: Citywire
Citywire is an exceptional employer for a Managing Sub-Editor, offering a dynamic work environment that values honesty, integrity, and fairness. With generous holiday entitlements and opportunities for professional growth, you will thrive in a culture that encourages collaboration and values every voice. Join us to shape the editorial voice of a leading player in the global asset management industry while developing your leadership and digital publishing skills.
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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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Citywire. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Citywire
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Citywire!
✨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.