Analytical Engineer at Praesignis

Job Description

• We are seeking an Analytical Engineer to transform complex, raw data into structured, governed, and analytics-ready datasets that enable advanced reporting, meaningful insights and data-driven decision-making across the organisation.
• This role bridges the gap between Data Engineering and Analytics by combining strong data modelling, data transformation and analytical expertise to design and deliver high-quality enterprise data products.

Key Responsibilities

Data Modelling & Design

• Design and implement scalable data models (e.g., dimensional and Data Vault) to support analytical use cases.
• Ensure alignment to enterprise data architecture and modelling standards.

Data Transformation & Engineering

• Develop and maintain ETL/ELT processes to ingest, cleanse, and transform data from multiple sources into trusted datasets.
• Deliver curated, analytics-ready data layers for consumption by downstream users.

Analytics Enablement

• Enable business intelligence, reporting, and advanced analytics use cases through high-quality data provisioning.
• Partner with data scientists and analysts to support modelling, insights, and data-driven decision-making.

Data Quality, Governance & Controls

• Ensure data integrity, lineage, and consistency across datasets.
• Apply governance frameworks and standards to support auditability and regulatory compliance.

Stakeholder Collaboration

• Engage with business and technical stakeholders to translate requirements into data solutions.
• Support delivery within cross-functional squads aligned to enterprise data products.

Required Skills & Experience

• Strong experience in data modelling (Data Vault, dimensional modelling)
• Proficiency in SQL and Python for data transformation and analysis
• Experience with modern data platforms (e.g., Azure, Databricks, Microsoft Fabric)
• Solid understanding of data warehousing, ETL/ELT, and analytics pipelines
• Familiarity with data governance, quality frameworks, and lineage
• Proven ability to combine engineering discipline with analytical problem-solving

Key Competencies

• Analytical thinking and problem-solving
• Attention to data quality and detail
• Strong stakeholder engagement and communication
• Delivery focus within agile environments
• Governance and compliance awareness

Deliverables / Success Measures

• Delivery of trusted, analytics-ready datasets aligned to business requirements
• Implementation of scalable and reusable data models
• Adherence to enterprise data governance and quality standards
• Enablement of timely and accurate reporting and analytics outcomes
• Contribution to data product delivery within GDA squads

Nice-to-Have

• Experience in banking or financial services data environments
• Exposure to real-time/streaming data processing frameworks
• Understanding of DataOps and CI/CD practices
• Certification in data modelling or cloud platforms