Senior Data Engineer SQL

Shape trusted banking data with expert SQL, scalable pipelines, and enterprise-grade modelling.
Senior Data Engineer SQL
Job description:
SQL Development & Data Modelling
Develop and optimize complex SQL transformations supporting analytics, reporting, and financial/regulatory needs.
Work with and contribute to existing enterprise data models (Data Vault, dimensional, 3NF, domain driven structures).
Ensure SQL logic is performant, maintainable, and aligned with modelling standards.
Implement and maintain dataquality checks, validation rules, and SQL testing.
Maintain clear documentation, lineage, and metadata to support transparency and governance.
Support reduction of architectural debt and help maintain a clean, consistent data environment.
Data Pipelines & Integration Engineering
Build and maintain SQL centric ETL/ELT data pipelines.
Use Python where beneficial for automation, API integration, or workflow efficiency.
Implement ingestion patterns for batch, incremental, and near real time data flows.
Ensure monitoring, observability, and reliable operation of data workflows.
Business Intelligence Enablement
Experience with Tableau or other BI tools is a strong plus, especially for:
Structuring SQL datasets optimized for BI consumption.
Supporting data preparation, extract logic, and backend performance.
Assisting dashboard/report designers with troubleshooting, prototyping, and governance.
Cross Functional Collaboration
Partner with finance, risk, compliance, and business teams to translate requirements into high quality data solutions.
Work closely with data architects to ensure alignment with modelling standards.
Support troubleshooting across ingestion, modelling, and operational processes.
Contribute to platform evolution, engineering improvements, and roadmap initiatives.
Communicate effectively with both technical and business stakeholders.
Data Platform & Operational Excellence
Collaborate with platform and infrastructure teams to operate SQL workloads reliably in production.
Apply engineering best practices: version control, documentation, testing, code quality.
About the customer:
This established financial services organization operates in a highly regulated banking environment, delivering data-driven solutions that support finance, risk, compliance, reporting, and business operations. Its technology teams manage complex enterprise data platforms and structured data models, with a strong focus on reliability, governance, transparency, and performance. The organization is continuing to modernize its data landscape through scalable SQL development, automated pipelines, robust testing, and improved observability. Collaboration is central, bringing together data engineers, architects, platform specialists, and business stakeholders to translate complex requirements into trusted data products. Based in Luxembourg, the working environment combines international exposure, agile delivery, and high engineering standards. The culture values ownership, clear communication, continuous improvement, and practical problem solving across the full enterprise data lifecycle.
Requirements:
Bachelor’s or Master’s degree in a relevant field.
5+ years in
data engineering or SQL heavy backend roles, in banking environment.
Proven experience working with
structured enterprise data models.
Hands-on experience with
Data Vault
(Data Vault 2.0 preferred), including design, implementation, and maintenance of enterprise-scale Data Vault solutions.
Strong experience with dbt (
Data Build Tool
), including development, testing, documentation, and deployment of data transformation pipelines.
Expert-level SQL skills
, including complex query development, performance tuning, and optimization.
Strong experience developing scalable
SQL-driven data transformations and ELT/ETL pipelines.
Experience working with and extending enterprise data models, including dimensional modeling,
SCDs, and 3NF architectures.
Strong communication and stakeholder engagement abilities.
Analytical, detail oriented, and proactive problem solver.
Comfortable in agile, iterative delivery environments.