At a Glance
- Tasks: Own and maintain data pipelines, collaborate on software platform evolution, and ensure data quality.
- Company: Specialist B2B Financial Services Tech platform with a focus on innovation.
- Benefits: Competitive salary, annual bonus, generous holiday, and medical insurance.
- Other info: Join a dynamic team with opportunities for growth and innovation.
- Why this job: Work on cutting-edge tech in finance and solve real-world data challenges.
- Qualifications: Experience with Spark, Scala, and strong SQL skills; finance background preferred.
The predicted salary is between 63000 - 77000 £ per year.
Talensa are partnered with a specialist B2B Financial Services Tech platform firm to hire an experienced Data Engineer (Mid-Senior) to take end-to-end ownership of a modern data platform built on Azure Databricks, Kafka and with BI-layer.
This person would be the primary Data Engineer, maintaining and further developing a well-established cloud and CI/CD setup to implement and evolve reliable, well-tested data pipelines in Spark/Scala and to extend the dimensional data models.
You will be part of the technical team collaborating closely with platform engineering, data science, and business stakeholders to further develop the synergy between the internal software platform and the Databricks data platform. This role will suit a hands-on Data Engineer who is looking to work on interesting Tech, solve data problems, while being a key member of the team, comfortable working independently day to day, following agreed designs, patterns, and standards.
What you’ll do:
- Own and maintain data pipelines ingesting from various sources into Databricks
- Collaborate closely with software engineers to evolve our software platform and address technical challenges
- Design, develop, and optimize Spark/Scala jobs for ingestion and transformation, using the existing GitHub Actions and Terraform setup for deployments
- Implement, run and monitor dbt tests to ensure data quality and consistent dimensional models
- Extend and maintain Delta Lake fact and dimension tables for analytics use cases, following a bronze/silver/gold-style data architecture
- Work closely with analysts, software engineers, and business users using Sigma to ensure they have trusted, well-documented datasets
- Monitor, troubleshoot, and improve pipeline reliability, performance, and cost within the existing observability framework
- Contribute to documentation of pipelines, datasets, and data contracts, acting as the main point of contact for data engineering topics
- Investigate and resolve data quality incidents and pipeline failures
- Proactively drive adoption of Databricks within the team and experiment with new Databricks features and tools where they bring clear value, including integrating AI tools for analysis and AI coding agents into day-to-day workflows
Tech stack:
- Azure Databricks (Spark, Delta Lake)
- SQL Server, Confluent Kafka
- Scala, SQL, dbt
- Git, GitHub Actions, Terraform
- Sigma (BI)
Must-have experience:
- Commercial experience as a Data Engineer working with Spark in production (ideally on Databricks)
- Essential experience working in finance, where you have developed a strong command of financial terminology, concepts, and stakeholder needs to collaborate effectively
- Good understanding of modern software platform architecture and the role of the data platform within the broader platform ecosystem
- Good Scala skills (or strong PySpark plus a genuine willingness to work primarily in Scala)
- Strong SQL skills for transformations, data modelling, and debugging data issues
- Experience building and supporting ingestion pipelines end to end
- Understanding of data modelling for analytics (star schema, facts/dimensions) and how schema changes affect downstream users
- Exposure to layered data architectures (e.g. bronze/silver/gold) and basic data governance practices (permissions, documentation, data ownership)
- Experience working in a CI/CD environment and with infra-as-code tools (e.g. GitHub Actions and Terraform on Azure)
- Track record of writing well-tested, maintainable data pipelines and jobs (unit/integration tests and data quality checks)
- Ability to work as a self-starter, taking ownership of the data platform, making pragmatic decisions, and driving work to completion with limited day-to-day supervision
Nice to have:
- Experience with Azure data services beyond Databricks (e.g. Key Vault, Storage).
- Experience with modern BI tools (e.g. Sigma, Looker, Power BI, Tableau).
- Experience working with AI coding agents (like Claude Code, Cursor, Copilot, etc) and writing developed thoughtful prompts
- Interest in analytics and data science, and working with others to turn data into insight
What’s on offer:
- Competitive Salary
- Annual Bonus and participation in Growth Share Scheme
- Pension
- Medical and Critical illness Insurance
- Generous Holiday days
- Opportunity to work in a specialist finance business with a great team
To note on scope: This is the only senior / experienced data engineer role in the Engineering Tech team with no immediate plans to hire underneath. Would suit someone who wants to work on interesting Tech & Data problems, platform innovations and support driving wider business growth.
Senior Data Engineer – Azure Databricks in London employer: Talensa
At Talensa, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation in the heart of London. Our hybrid working model allows for flexibility while you tackle exciting data challenges within a specialist finance tech environment, supported by competitive salaries, annual bonuses, and ample opportunities for professional growth. Join us to be part of a dedicated team where your contributions directly impact our success and the evolution of our cutting-edge data platform.
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We think you need these skills to ace Senior Data Engineer – Azure Databricks in London
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