We are seeking an experienced and highly motivated Data Engineer to design, build, and operate reliable data pipelines and analytics-ready datasets that power reporting and business decision-making. In this role, you will develop and optimize ETL/ELT processes using Python, Azure Databricks, and SQL, integrating data across platforms including Microsoft SQL Server and PostgreSQL. You will partner with analysts, data consumers, and engineering teams to translate business requirements into scalable, well-governed data products, applying strong engineering practices around testing, monitoring, performance, and data quality, while using AI-assisted development tools and generative AI to enhance productivity and solution quality. We are looking for someone with hands-on experience integrating AI capabilities into enterprise data workflows and a strong understanding of responsible AI principles, including data privacy, fairness, and explainability.
ResponsibilitiesKey duties and responsibilities:
Data Pipeline Development (ETL/ELT): Design, build, and maintain batch and/or incremental pipelines using Python, Azure Databricks, and SQL to ingest, transform, and curate data for analytics and downstream applications.
Orchestration, Scheduling & Reliability: Automate and schedule pipeline execution, implement idempotent processing, and build monitoring/alerting and operational runbooks to ensure reliable, observable data products.
Production Operations Support: Own day-to-day production support for data pipelines and reporting workflows, including incident triage and resolution, root-cause analysis, and proactive monitoring to meet SLAs and ensure data reliability.
Databricks Engineering: Develop and optimize notebooks and jobs; implement reusable libraries, parameterized workflows, and cluster/job configurations; apply performance tuning techniques (partitioning, caching, query optimization) as appropriate.
SQL performance optimization: Optimize schemas, tables, views, and SQL logic across Microsoft SQL Server and PostgreSQL. Troubleshoot performance issues and ensure data integrity through constraints, indexing, and query tuning.
Data Quality & Controls: Define and implement validation rules, reconciliation checks, and automated tests to detect anomalies, enforce SLAs, and improve trust in reporting and analytics outputs.
AI Enablement & Productivity: Use AI-assisted development tools and generative AI to improve engineering productivity, data pipeline quality, and documentation while maintaining strong validation and governance practices.
Required experience & competencies
- 5+ years in Data engineering roles.
- Strong Python for ETL, automation, and data transformations.
- Hands-on SQL Server development: schemas, procedures, indexing, optimization.
- Experience with Azure data services and Databricks operations.
- Proven experience with AI-assisted software development tools and workflows.
- Demonstrated ability to use generative AI to improve developer productivity and solution quality.
- Solid understanding of responsible AI principles, including data privacy, fairness, and explainability.
- Hands-on experience integrating AI capabilities into enterprise data or analytics applications.
- Degree in Computer Science, Management of Information Systems, or a related analytical field or equivalent experience.
Nice-to-haves:
- Azure Data Factory
- Experience with data quality testing and validation frameworks.
- Familiarity with Lakehouse concepts and Delta tables.
- Experience with PostgreSQL, including query and schema optimization.
- CI/CD for data pipelines using Azure DevOps or Gitlab or similar.
- Ability to integrate AI/ML model endpoints into data platforms and pipelines using Azure ML or similar.
- Advanced SQL for analytics, modeling, and performance tuning.
As a leading global reinsurer, SCOR offers its clients a diversified and innovative range of reinsurance and insurance solutions and services to control and manage risk. Applying “The Art & Science of Risk,” SCOR uses its industry-recognized expertise and cutting-edge financial solutions to serve its clients and contribute to the welfare and resilience of society in around 160 countries worldwide.
Working at SCOR means engaging with some of the best minds in the industry – actuaries, data scientists, underwriters, risk modelers, engineers, and many others – as we work together to find solutions to pressing challenges facing societies.
As an international company, our common culture is defined by “The SCOR Way.” Serving both to build momentum that drives the Group forward and as a compass to guide our actions and choices, The SCOR Way is anchored by five core values, reflecting the input of employees at all levels of the Group. We care about clients, people, and societies. We perform with integrity. We act with courage. We encourage open minds. And we thrive through collaboration.
SCOR supports inclusion and the diversity of talents, and all positions are open to people with disabilities.
Skills Required
- 5+ years of experience in data engineering roles
- Strong Python experience for ETL, automation, and data transformations
- Hands-on Microsoft SQL Server development, including schemas, procedures, indexing, and optimization
- Experience with Azure data services and Databricks operations
- Experience with AI-assisted software development tools and workflows
- Experience using generative AI to improve developer productivity and solution quality
- Understanding of responsible AI principles, including data privacy, fairness, and explainability
- Experience integrating AI capabilities into enterprise data or analytics applications
- Degree in Computer Science, Management Information Systems, a related analytical field, or equivalent experience
- Experience with Azure Data Factory
- Experience with data quality testing and validation frameworks
- Familiarity with Lakehouse concepts and Delta tables
- Experience with PostgreSQL query and schema optimization
- CI/CD experience for data pipelines using Azure DevOps, GitLab, or similar tools
- Experience integrating AI/ML model endpoints using Azure Machine Learning or similar platforms
- Advanced SQL for analytics, modeling, and performance tuning
What We Do
SCOR, one of the world’s largest reinsurers, serves more than 5,000 clients worldwide, providing a diversified and innovative range of solutions to control and manage risk. SCOR delivers advanced financial solutions, analytics and services across all dimensions of risk in Life & Health, Property & Casualty, and Investments. Reinsurance lies at the intersection of technical expertise and scientific progress. Models, data, and pricing and reserving tools are essential, yet they are never sufficient on their own. Sound risk decisions require expert judgment, experience and perspective. This is what we call the Art and Science of Risk. Reinsurance is a knowledge industry, where expertise grows through accumulation, transmission and practice. Across the Group, 3,600 experts based in more than 35 offices worldwide contribute to this collective intelligence. Actuaries, underwriters, risk management specialists, and Tech & Data experts transform data into insight, explore extreme scenarios, define the boundaries of insurability and help anticipate emerging risks. Together, they strengthen the resilience of SCOR, our clients and the societies we serve. This expertise is built through shared experience,continuous questioning and collective reflection. Like artists, we belong to schools of thought, learning first to observe, then to replicate, and ultimately to innovate. This ongoing transmission of knowledge enables SCOR to develop a distinctive approach, combining rigor, creativity and long-term vision in the service of risk mastery. This shared commitment underpins SCOR’s role as a global reinsurer. By turning risk into resilience and sustainable value, our collective of experts acts with responsibility and purpose. Together, we help protect the future, and shape it, for our clients, for society and for generations to come.
