Data Engineer

Data is a core pillar of SCOR’s Forward 2026 strategic plan and beyond. Our ambition is to enable trusted, governed, and accessible data so that business teams can generate insights faster and make better decisions.

As a Data Engineer within the Chief Data Officer organization, you will design and deliver analytics and AI ready datasets and curated data products that power dashboards, reporting, and analytical use cases within one primary business domain: Property & Casualty, Life & Health, or Finance.

A critical aspect of this role is a strong understanding of the business domain you are supporting. You are expected to develop deep knowledge of key processes, metrics, and decision drivers within your assigned domain, and to translate this business understanding into robust analytical data models, consistent KPIs, and meaningful datasets.

Increasingly, analytical use cases at SCOR are augmented by AI driven capabilities. As such, this role assumes that the candidate understands and has practical experience working with AI agents or AI assisted analytics (e.g. agents supporting data exploration, metric analysis, automation, or decision support). The Data Engineer is expected to leverage these capabilities thoughtfully to accelerate insight generation, improve usability of analytical datasets, and enhance business decision making.

You will work at the intersection of data engineering and analytics, shaping data models, implementing transformations, ensuring data quality, and making datasets discoverable and usable for business consumers on SCOR’s enterprise data foundation (Genesis) and modern data platforms.
 

The Data Engineer is a professional who is:

• Outcome‑driven: Focuses on delivering datasets and metrics that materially improve business steering and decision‑making (timeliness, trust, usability). 

• Data‑product minded: Treats analytical datasets as products with clear contracts (definitions, grain, lineage, quality expectations, documentation).

• Quality & governance oriented: Designs datasets that are consistent, auditable, and aligned with governance expectations (definitions, controls, traceability). 

• Collaborative bridge‑builder: Works effectively with business stakeholders, Data Foundation, Governance, Analytics & AI, and Platform teams to translate needs into robust analytical assets.
• Clear communicator: Can explain complex data concepts in a practical way and build trust with both technical and non‑technical stakeholders.

Key duties and responsibilities

• Design, build, and maintain analytics‑ready datasets that directly support reporting, dashboards, and decision‑making within your business domain (P&C, L&H, or Finance).
• Translate business concepts, processes, and decisions into clear analytical data models, dataset structures, and reusable metrics.
• Ensure data quality, traceability, and documentation for analytical datasets (definitions, grain, assumptions, lineage, known limitations).
• Leverage AI agents and AI‑assisted analytics to accelerate data exploration, support metric analysis, and enhance decision‑making, in line with data quality and governance standards.
• Partner closely with business stakeholders (e.g. actuarial, finance, underwriting, performance steering) to understand analytical needs and deliver data products with real business impact.
• Contribute to and apply data governance standards and best practices at the analytical layer, in collaboration with Data Foundation and Governance teams.
• Support adoption of analytical datasets by ensuring they are understandable, discoverable, and fit for self‑service consumption.
• Collaborate with Platform, Analytics & AI teams to ensure tooling, standards, and architecture effectively support analytics delivery.

Required experience & competencies

•    Proven experience delivering analytics ready datasets used for AI, dashboards, reporting, and decision making in a complex data environment. 
•    Strong business understanding in at least one domain: Property & Casualty, Life & Health, or Finance, with the ability to reason about domain KPIs, metrics, and processes. 
•    Hands on experience using AI agents or AI assisted analytics to support data exploration, metric analysis, automation, or decision support, with an understanding of how these capabilities complement high quality, well governed analytical datasets. 
•    Strong hands on experience with SQL, Python and Pyspark for building scalable data pipelines, analytical datasets and data products in modern clould data platforms 
•    Proven experience in designing and implementing data models, medallion architectures in Databricks, Palantir Foundry, or comparable platform including ingestion, transformation, and domain driven data modeling principles where appropriate. 
•    Demonstrated ability to gather and refine business requirements, translate them into technical solutions and independently deliver end-to-end data products from source ingestion through analytical consumption 
•    Strong stakeholder collaboration skills, with the ability to align business and technical teams on definitions, priorities, and delivery. 
•    Apply Software engineering best practices including version control, testing, code reviews and automated deployment of data pipeline. 
•    Proficiency in English; French is a plus.
 

 

Required Education 

·    Degree in technical or quantitative discipline (e.g. data, engineering, applied mathematics, statistics) or equivalent professional experience.