Senior Data Engineer - Financial Services Data Lake (all genders)

Effective Field

• As a Senior Data Engineer, you contribute to the design, build-out, and continuous evolution of a modern data lake solution for a financial-services environment. Working as part of a cross-functional team of data engineers, architects, analysts, and domain experts, you transform complex data requirements into secure, scalable, and maintainable data products.
• You design, implement, test, deploy, and operate robust ETL/ELT pipelines for structured and unstructured data. Your work covers end-to-end data ingestion, transformation, validation, storage, and provisioning—from source-system analysis to productive deployment and operational improvement.
• You work hands-on with technologies including Apache NiFi, Apache Spark, Python, SQL, Kafka, PostgreSQL, MongoDB, Parquet, REST APIs, and Polaris. You build reusable data loaders and integration components, and you support the team in applying consistent engineering, testing, and deployment standards. Depending on the solution architecture, you use cloud services on AWS and/or Azure.
• A key part of your role is collaborating with business and IT stakeholders to gather, challenge, and structure requirements for data domains, interfaces, and pipelines. You translate these into clear technical specifications, data mappings, and implementable backlog items. In a financial-services context, you take care to ensure data quality, traceability, security, and reliable processing.
• You use GenAI applications and agents responsibly as part of your development workflow—for example, to accelerate code reviews, documentation, test creation, troubleshooting, and analysis—while validating all outputs and adhering to project governance.

What distinguishes you

• Degree in Computer Science, Business Informatics, Data Engineering, Data Science, or a comparable qualification
• Typically 5+ years of practical experience in data engineering, software development, data integration, or data-platform development
• Proven experience designing, implementing, testing, and operating production-grade ETL/ELT pipelines
• Strong skills in Python and SQL, including writing maintainable, performant, and testable code
• Hands-on experience with Apache Spark and PySpark, including distributed data processing and performance-conscious transformations
• Solid experience with Apache NiFi for data ingestion, routing, integration, and operational monitoring
• Experience implementing event-driven or near-real-time data flows with Apache Kafka
• Good knowledge of relational and non-relational databases, ideally PostgreSQL and MongoDB
• Practical experience working with modern data formats and storage concepts, especially Parquet
• Experience integrating source systems through REST APIs, databases, files, and streaming interfaces
• Strong understanding of working with both structured and unstructured data
• Experience designing and implementing reusable data loaders, ingestion frameworks, and standardised pipeline patterns
• Hands-on cloud experience with AWS and/or Azure, including relevant storage, compute, integration, and security services
• Ability to gather and structure complex data and pipeline requirements, translate them into technical designs, and communicate them clearly to technical and non-technical stakeholders
• Strong awareness of data quality, validation, reconciliation, observability, lineage, and error handling
• Experience with software engineering practices such as Git, code reviews, automated testing, CI/CD, and deployment processes
• Experience with Polaris or comparable data-platform and governance components
• Experience with data lake or lakehouse architectures, such as raw/standardised/curated or bronze/silver/gold layers
• Knowledge of data modelling, including dimensional models, canonical data models, and history management
• Experience with infrastructure automation, containerisation, orchestration, and cloud-native deployment patterns
• Familiarity with financial-services data domains, regulatory requirements, auditability, data protection, risk, or compliance processes
• Experience with metadata-driven ingestion frameworks and configuration-based pipeline development
• Knowledge of data catalogue, governance, lineage, access-management, or data-quality tooling
• Practical experience applying GenAI assistants or agents in development activities, such as generating test cases, documenting pipelines, analysing logs, or supporting code reviews
• Relevant cloud, data-engineering, Kafka, Spark, or database certifications
• A collaborative, accountable working style and the ability to support colleagues through technical sparring, reviews, and shared standards
• Fluent English; German is an advantage depending on the project context

What you can expect

• Professional environment with challenging national and international digital projects for well-known customers and hidden champions;
• A passionate team with Can-Do Attitude that welcomes you with open arms;
• A modern working environment with a feel-good factor;
• Development opportunities that suit you;
• Flexible working hours, attractive benefits and of course lots of fun.