At a Glance
- Tasks: Build and support modern data platforms in financial services environments.
- Company: Alpha Financial Markets Consulting, a leading global consultancy in finance.
- Benefits: Competitive salary, performance bonuses, hybrid work, and continuous learning opportunities.
- Other info: Collaborative environment with excellent career growth and wellbeing support.
- Why this job: Join a dynamic team solving real data challenges with cutting-edge technology.
- Qualifications: 2+ years in data engineering with experience in Databricks or Snowflake.
The predicted salary is between 60000 - 75000 £ per year.
Alpha Financial Markets Consulting (Alpha) is a leading global consultancy to the financial services industry.
We are a boutique management consulting firm that offers the world’s top industry players a competitive edge through our expertise and industry insight.
Our team is of a uniquely high calibre and works across regulated financial markets, bringing deep expertise in insurance, alternative asset markets — including private equity, private credit, infrastructure and real estate — and other specialised financial sectors.
This focus enables us to bring relevant, hands-on experience to our clients’ most important challenges.
We have our headquarters located the United Kingdom, as well as offices in major global financial centres across the United States, France, Netherlands, Luxembourg, Switzerland, and Asia.
About the role
We’re looking for a Data Engineer with 2+ years’ experience to join our growing data engineering capability.
This role is focused on client delivery.
You’ll work closely with client teams, alongside their internal engineers and Alpha consultants, to build and support modern data platforms in regulated financial services environments.
The work is hands-on and delivery-focused: building pipelines, developing data models, and helping turn business requirements into trusted, production-ready data solutions.
This is a strong opportunity for an engineer who wants more than just pipeline delivery.
You’ll work directly with clients, solve real platform and data problems, and build experience across a range of delivery challenges in complex regulated environments.
What you'll do
- Build reliable ingestion pipelines that move client data from source systems into the platform efficiently and securely
- Develop data pipelines to agreed SLAs, with a focus on reliability in production
- Work with domain SMEs and delivery teams to translate business logic into pipeline logic that produces accurate, trusted outputs Senior Data Engineer. docx
- Design data models, including Data Vault 2.0 and Kimball dimensional models, that support auditability and trusted reporting
- Support testing, monitoring, troubleshooting, and documentation across the delivery lifecycle
- Contribute to engineering standards and delivery best practices as part of a wider team
- Work with the team to implement LLM-based processing in pipelines where appropriate, alongside deterministic logic
- Work on delivery teams building AI-enabled solutions on the platform, helping to apply trusted data to real client problems
- Share learnings from delivery work and contribute to internal assets, tooling, and practices over time.
- What you'll bring
- 2+ years in data engineering, including production experience with Databricks or
- Snowflake (one platform is sufficient)
- Strong SQL and Python; comfort with Py Spark or Snowpark
- Experience with at least one cloud vendor, Azure, AWS, or GCP, including familiarity with core services beyond the data platform itself (networking, IAM, storage)
- Working knowledge of dimensional modelling (Kimball) and/or Data Vault 2.0
- Strong attention to quality, documentation, and delivery discipline
- Additional experience we’d value
- Snowflake or Databricks certifications
- Familiarity with private markets data (fund administration, private equity/real estate, secondaries) or London Market insurance
- Experience with some supporting tooling across areas such as infrastructure-as-code, transformation, Dev Ops, data quality, governance, or orchestration would be beneficial.
- Exposure to regulated environments such as financial services, insurance, or similarly audited sectors would be beneficial.
- What the engagement looks like
You'll work within client teams, alongside their internal engineers and Alpha consultants in a regulated environment where change control, data lineage and documentation matter.
Expect a mix of independent build work and collaborative delivery, with regular touchpoints against a delivery plan.
What We Offer
- Competitive base salary
- Annual performance bonus tied to individual and company outcomes.
- Comprehensive benefits package including private medical insurance, life assurance, and income protection.
- Hybrid working model with flexible arrangements to support work-life balance.
- Generous pension scheme with enhanced employer contributions.
- Continuous learning budget and access to industry conferences, certifications, and training programmes.
- 25 days annual leave plus bank holidays, with option to buy additional days.
- Employee assistance programme and wellbeing support.
Data Engineer (Databricks / Snowflake) in London employer: Alpha Financial Markets
Alpha Financial Markets Consulting is an exceptional employer, offering a dynamic work environment where data engineers can thrive. With a focus on client delivery and hands-on problem-solving, employees benefit from a competitive salary, comprehensive benefits, and a strong emphasis on continuous learning and professional growth. The hybrid working model and supportive culture ensure a healthy work-life balance, making it an ideal place for those seeking meaningful and rewarding employment in the financial services sector.
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We think this is how you could land Data Engineer (Databricks / Snowflake) in London
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We think you need these skills to ace Data Engineer (Databricks / Snowflake) in London
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
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
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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.
