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описание
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.
задачи
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 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.
требования
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
A collaborative, accountable working style and the ability to support colleagues through technical sparring, reviews, and shared standards
Будет плюсом: Relevant cloud, data-engineering, Kafka, Spark, or database certifications; German is an advantage depending on the project context
условия
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.
