Data Engineer

In the frame of the ongoing transformation at Group level, technology adoption and implementation are key success factors, as well as processes transition. As a member of DDPO, **Data Engineer will build, deliver and maintain products & data products (data pipelines, services, APIs )**He will work in close collaboration with data product managers to develop new features linked to these products, especially features linked to:

- Data ingestion, transformation, exposition
- Data servicing (through API) or restitution (through apps / dashboards)
- Parallel calculation on big volumes of data

The Data Engineer is responsible for:

- Building, delivering, maintain, documenting data products & features (data pipelines, data services, APIs ) following state-of-the-art patterns (medaillon architecture, gitflow)
- Acting as SME and representative of Data Chapter

The Data Engineer needs to:

- Be problem-solving oriented with strong analytical thinking
- Be autonomous and rigorous in the way he/she approaches technical challenges
- Advise on the architecture of end-to-end data flows
- Collaborate with various stakeholders (product owners, solution owners, data solution analysts, developers, technical leads, architects) to deliver data artefacts in a team spirit
- Commit and bring his/her skills to contribute to the success of the Group

Under the responsibility of the Head of Data Delivery & Engineering, your mission will be to:

- Build, deliver, maintain and document data artefacts or features linked to the products you will be assigned to, under the prioritization of the Data Product Manager you will be working with:

- Develop data pipelines leveraging ELT / ETL techniques to ingest, transform and display data for well-defined purposes following state-of-the art approach (medaillon architecture for data, gitflow for development, unit tests where applicable )
- Tackle key technical questions linked to data, like parallelization, calculation on big volumes of data, optimization of queries, etc.
- Develop, when relevant, taylor-made services / APIs to expose the data for various means (BI, APIs, services, data science )
- Enrich SCOR ontology and document data artefacts (code documentation for data pipelines / services / APIs, contribution to the data definition, processes, etc.)
- Reuse when relevant components or assets (code, frameworks, data objects) to leverage as much as possible on the DDPO ecosystem
- Acting as SME and representative of Data Chapter
- Contribute to the design of solutions (end-to-end data flows) in close collaboration with Architects, Data Modelers, Product Owners & Managers and advise on best practices to external stakeholders
- Contribute to the overall data community by sharing good practices, return of experiences, expertise on relevant technology, etc.
- Perform peer review amongst other data engineers to ensure consistency and quality of development
- Be aware of the DDPO ecosystem to best ask advise when relevant to the appropriate experts (Data Modelers, ML Engineers, Architects, etc.)
- Additional activities related to your day-to-day mission
- Ensure a technological watch on Data platform solutions, especially related to data engineering topics
- Participate to Scrum rituals (dailys, sprint planning, sprint reviews, retrospectives, etc.)
- Contribute to ICS

**Required experience & competencies**
- +5 years of experience as a data engineer, data oriented mindset
- Proven experience in development and maintenance of data pipelines
- Good development practices (gitflow, unit tests, documentation )
- Proven experience in agile projects (Scrum and/or Kanban)
- Knowledge of (Re)insurance industry and / or Financial Services is a plus
- Awareness on data management and data privacy topics

Technical Skills:

- Strong level in Python and Pyspark, ability to develop data pipelines under various platform (Databricks, Palantir Foundry,)
- Strong level in SQL (knowledge of ANSI92 SQL, execution plan analysis)
- Good knowledge of parallelization, distributed programming techniques
- Good knowledge of datalakes environments and concepts (delta lakes, medaillon architecture, blob storage vs file shares )
- Good knowledge in decisional data modeling (Kimball, Inmon, Data Vault concepts ) and associated good practices (slowly changing dimensions, point in time tables, chasM/Fan trap management, change data capture management )
- Knowledge of CI / CD pipelines (AzureDevOps, Jenkins, Artifactory, Azure Container Registry )
- Knowledge of REST API development (Flask, FastAPI, Django )
- Knowledge of containers (Docker Compose, Kubernetes) is a plus
- Knowledge of reporting tools is a plus (Tableau, Power BI )
- Knowledge of streaming technologies is a plus (Kafka)
- Knowledge of typescript is a plus

Behavioral & Management Skills:

- Strong analytical thinking, solution-oriented and force of proposal
- Capacity to navigate in a matrix and international environment
- Autonomy, rigorous mindset, com