At a Glance
- Tasks: Build and optimise large-scale Scala Spark data pipelines on Databricks.
- Company: Join Citi, a leader in global finance and technology innovation.
- Benefits: Enjoy 27 days annual leave, private medical care, and a hybrid working model.
- Other info: Collaborate with global teams and contribute to exciting tech projects.
- Why this job: Make a real impact by modernising critical financial data systems.
- Qualifications: Experience with Scala Spark and familiarity with AWS services required.
The predicted salary is between 75600 - 92400 £ per year.
Engineer the future of global finance. At Citi, our Tech team doesn’t just support finance – we are helping to redefine it. Every day, $5 trillion crosses through our network. We do business in 180+ countries operating at a scale few can match. From deploying advanced AI to helping shape global markets, we build systems that matter. Look to join a team where your work helps influence economies, your ideas can drive innovation and outcomes, and your growth is backed by mentorship, continuous learning and flexibility with potential hybrid work opportunities. Help solve real-world challenges that touch millions and get the opportunity to build the future of finance with Citi Tech.
About the Team: We are an established data engineering team with a strong focus on reconciliation — building and maintaining large-scale data reconciliation platforms that ensure accuracy, consistency, and integrity across critical financial data. This is not a new team; we have a solid foundation and a proven track record, and we are now entering an exciting phase of modernization and evolution.
What We Do: Our team owns and drives a large-scale reconciliation data platform built on Scala Spark. We are currently on an ambitious journey to migrate and modernize our platform from a legacy Cloudera Hadoop environment to Databricks on AWS — adopting cloud-native patterns, simplifying pipelines, and unlocking new capabilities along the way. The work is technically complex, high-impact, and directly tied to data quality outcomes that matter across the organization.
Where We Fit: Within the broader organization, our team sits at the intersection of data engineering and financial data operations — playing a critical role in ensuring that large volumes of data are processed, validated, and reconciled accurately and at scale. The reliability of our platform has a direct impact on downstream reporting, risk, and decision-making.
Who We Work With: On a day-to-day basis, the team collaborates closely with:
- Senior Architects & Platform Teams – to shape technical direction and design decisions
- DevOps & Cloud Engineers – to support infrastructure, deployments, and cloud-native adoption on AWS
- Stakeholders & Business Teams – to understand reconciliation requirements and translate them into scalable engineering solutions
What Makes Our Team Interesting: We are working on a high-impact, technically challenging modernization — moving a production-grade reconciliation platform from Hadoop to Databricks on AWS. The domain — data reconciliation at scale — is inherently complex and intellectually rewarding, involving large datasets, performance-critical pipelines, and precision engineering. We value strong individual contributors — this is a hands-on role where your Scala Spark expertise will directly shape the platform's future. We are a team that is enthusiastic about what we are building — there is a genuine drive to do things the right way, adopt modern patterns, and continuously raise the bar on engineering quality. You will have the opportunity to contribute to architectural decisions, not just execute them.
Role Overview/What will you do:
- Build and optimise large-scale Scala Spark data pipelines on Databricks, focusing on the reconciliation domain — writing clean, modular, and production-ready code
- Drive modernisation efforts by refactoring legacy Hadoop-based pipelines into cloud-native Databricks/AWS architectures using Delta Lake and Databricks Workflows
- Tune and simplify complex or over-engineered Spark jobs — analysing performance bottlenecks and transforming them into efficient, standardised patterns
- Translate designs into reality — take high-level architectural decisions and convert them into detailed technical implementations, data models, and reusable pipeline components
- Collaborate daily with architects, platform, and DevOps teams on design discussions, technical decisions, and production releases
- Write and maintain tests — unit, integration, and data validation — to ensure quality and reliability of reconciliation pipelines
- Contribute to engineering standards — shared frameworks, reusable libraries, and best practices across the data platform
This role exists to strengthen and evolve a business-critical data reconciliation platform — ensuring it is scalable, performant, and future-ready. The individual will be a key hands-on contributor driving the platform's migration from legacy Hadoop to Databricks on AWS, while continuously improving the quality and efficiency of reconciliation data processing at scale.
This is a global, cross-functional role with day-to-day interaction across:
- Global engineering and architecture teams — collaborating on platform design, standards, and technical direction
- DevOps and cloud infrastructure teams — supporting deployments and cloud-native adoption on AWS
- Regional and local stakeholder teams — engaging with business and data operations colleagues who rely on the reconciliation outputs
Key Skills and Experience:
- Experience with Scala Spark or PySpark or Java Spark — comfort working across multiple Spark language APIs
- Familiarity with AWS services such as S3, Glue, IAM, CloudWatch, or Step Functions in a data engineering context
- Exposure to data reconciliation or financial data domains — understanding of recon logic, data validation, and accuracy requirements at scale
- Experience with CI/CD pipelines and DevOps practices — automated testing, deployment pipelines, and version control in a data engineering context
- Knowledge of data modelling concepts and pipeline orchestration patterns beyond Databricks Workflows (e.g. Apache Airflow)
- Prior experience working in large-scale Hadoop to cloud migration programmes or legacy platform modernisation projects
- Familiarity with streaming data processing (e.g. Apache Kafka, Spark Structured Streaming) alongside batch workloads
- Experience contributing to shared engineering frameworks or reusable libraries within a platform team setting
What we’ll provide you: By joining Citi, you will not only be part of a business casual workplace with a hybrid working model (up to 2 days working at home per week), but also receive a competitive base salary (which is annually reviewed), and enjoy a whole host of additional benefits such as:
- 27 days annual leave (plus bank holidays)
- A discretional annual performance related bonus
- Private Medical Care & Life Insurance
- Employee Assistance Program
- Pension Plan
- Paid Parental Leave
- Special discounts for employees, family, and friends
- Access to an array of learning and development resources
Alongside these benefits Citi is committed to ensuring our workplace is where everyone feels comfortable coming to work as their whole self, every day. We want the best talent around the world to be energized to join us, motivated to stay and empowered to thrive.
Lead Data Engineer – Spark / Scala - Vice President employer: Citigroup
Citi is an exceptional employer, offering a dynamic work environment in the heart of London where innovation meets collaboration. As a Sales Director, you will benefit from extensive training and mentorship opportunities, fostering your professional growth while working alongside industry leaders in FX Sales. With a strong commitment to integrity and inclusivity, Citi provides a supportive culture that empowers employees to excel and drive meaningful results for clients.
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We think you need these skills to ace Lead Data Engineer – Spark / Scala - Vice President
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