Description:
To help fuel that growth, we need a Senior Data Engineer to join our Engineering team.
This team is the beating heart of Prima.
You’ll be joining over 350 engineers across software development, infrastructure, operations and security. Fueled by curiosity, experimentation and collaboration, you’ll help deliver scalable, impactful solutions that shape the future of insurance.
Excited to make an impact? Here are the details
You’ll be joining our pricing and underwriting domain to bridge the gap between machine learning/data science and engineering. You will help build, publish, and maintain our complex data products and pipelines, key elements that have a significant impact on the company’s growth.
What You’ll Do
- Shaping the architecture of data products designed for data analytics and data science specifically focusing on use cases like forecasting, feature engineering, customer behaviour, and integration of new data sources.
- Leading the way in data transformation by setting up best practices in areas like Data modelling, performance optimisation, Data Governance etc, ensuring that the data used within Prima is consistent, available and reliable.
- Build reusable technology that enables teams to ingest, store, transform, and serve their own data products.
- Engaging with data scientists and machine learning engineers to explore the product landscape and refine data requirements for enhanced data infrastructure.
- Embrace continuous learning and experimentation to stay updated on emerging technologies, from testing open source tools to engaging in community-building activities like Meetups. Your passion for staying at the forefront of the field will drive your journey.
What We’re Looking For
- Expert in batch, distributed data processing and near real-time streaming data pipelines with technologies like Kafka, Flink, Spark etc. Experience in Databricks is a plus.
- Experience in Data Lake / Big Data Analytics platform implementation with cloud based solution; AWS preferred.
- Proficient in Python programming and software engineering best practices.
- Expertise with RDBMS, Data Warehousing, Data Modelling with relational SQL (Redshift, PostgreSQL) and NoSQL databases.
- Proficiency in DevOps, CI/CD pipeline management, and expertise in infrastructure as Code (IaC) deployment industry-best practices.
Nice-to-Have
- Hands-on experience in Data Quality and Data Governance techniques.
- Knowledge on MLOps and Feature engineering.
- Exposure to common data analysis and ML technologies such as on scikit-learn, pandas, NumPy, XGBoost, LightGBM.
- Exposure to tools like Apache Oozie, Apache Airflow.