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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - Spark / Scala - Vice President - **Company:** Citigroup Inc. - **Location:** Belfast, United Kingdom (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Amazon S3, Automation of Tests, Big Data, Cloud Computing, Cloud Engineering, Data Validation, Information Engineering, Data Infrastructure, DevOps, Apache Hadoop, Identity and Access Management, DataOps, Cloudera, Data Streaming, Working Model 2D, Data Processing, Delivery Pipeline, Apache Spark, Data Lakes, Pyspark, Information Technology, Production Code, Apache Kafka, Spark Streaming, Cloudwatch, Software Version Control, Data Pipelines, Databricks - **Published:** November 9, 2026 - **Apply:** https://citi.wd5.myworkdayjobs.com/2/job/Belfast--United-Kingdom/Lead-Data-Engineer---Spark---Scala---Vice-President_261000628/apply ## About the Role * 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 ## Description 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