Functional Data Engineer
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The Data department at BNP Paribas Fortis builds and maintains future-ready and traditional data platforms, tools, and services to ensure easy, secure data access. It empowers data consumers with self-service capabilities while upholding governance and quality standards.
YOUR JOB IN A NUTSHELL
As a Data Engineer, you will design advanced data pipelines and infrastructure that power AI, real-time analytics, and strategic decision-making as part of our digital transformation. You will address challenges such as scalability, latency, and compliance, ensuring secure, high-quality data flows while collaborating in Agile teams. Your work will unlock data-driven insights, combining technical excellence with regulatory requirements to shape the bank’s future.
RESPONSIBILITIES
- Build and maintain optimized data stores by designing, developing and ensuring well-structured, high-performance data assets (e.g., databases) tailored for APIs, reporting, analytics, and BI tools.
- Enable data-driven decision-making by delivering reliable, accurate and accessible data for business intelligence, reporting and analysis services while maintaining related infrastructure.
- Collaborate with internal clients and teams as a trusted partner, understanding stakeholder needs, guiding them on data solutions, and working cross-functionally while adhering to Tribe methodologies and standards.
- Ensure compliance and risk management by strictly following data protection regulations (e.g., GDPR) and banking security policies, mitigating risks in data collection, processing and storage to safeguard client and institutional data.
- Drive continuous improvement by proactively identifying opportunities to enhance data quality, efficiency and innovation—through new tools, automation or process optimizations—while aligning with business and regulatory requirements.
- Collect data from the source and design it into models that will be used by developers. Translate business requests into technical models that developers can implement.
- Uphold data quality and governance by implementing validation rules, monitoring and metadata management to maintain accuracy, consistency and compliance.
- Support stakeholders and troubleshoot issues by resolving data incidents (e.g., broken pipelines, latency problems) and partnering with analysts, product owners or business teams to translate requirements into technical solutions, such as creating self-service datasets or optimizing queries for reporting tools (e.g., Power BI, Tableau).
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