Lead Data Engineer - Spark​/Scala - Vice President

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.

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.

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.

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  reliability of our platform has a direct impact on downstream reporting, risk, and decision-making.

On a day-to-day basis, the team collaborates closely with:

• Senior Architects & Platform Teams - to shape technical direction and design decisions
• Dev Ops & 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

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

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 Dev Ops 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…