Data Engineer
Honeycomb is a rapidly expanding, well-funded startup committed to setting the benchmark for excellence in the insureTech industry. Our foundation is built on cutting-edge technology, but our greatest asset is our exceptional team of professionals. By seamlessly integrating AI, state-of-the-art technology, and profound human expertise, each engineer at Honeycomb plays a pivotal role in shaping our trajectory.
Join us and experience the tremendous impact you can make!
Job Description:
We are seeking an experienced Data Engineer to help architect and build data workflows and infrastructure. You will play a critical role in shaping the information architecture that will serve as the foundation for our platform, AI models, and BI reports. The role will involve extracting raw data from vendor responses in real time and transforming it into valuable information stored in the appropriate databases.
Responsibilities:
- Information Architecture Design: Design and implement information architecture to support platform, AI, and BI data needs.
- Pipeline Workflow Design: Develop and maintain robust data pipelines for ingesting, processing, and storing vendor data.
- Data Extraction: Extract, transform, and load (ETL) raw vendor data into the correct databases for use by the platform and AI models.
- Data Analysis: Utilise data analysis tools and techniques to ensure data integrity, quality, and availability for various stakeholders.
Requirements:
- Proven experience in data acquisition, pipeline design, and data architecture.
- Proficiency with ETL tools and data pipeline technologies
(e.g., Apache Airflow, Prefect, Dagster). - Strong knowledge of databases (SQL, NoSQL) and data storage solutions.
- Experience with cloud platforms (AWS, GCP, Azure) for data infrastructure.
- Strong problem-solving skills with a focus on data optimization and performance.
- Proficiency in Python, Node.js, or similar languages.
Nice to Have:
- Experience working with data from external vendors or APIs.
- Proficiency in data analysis tools and frameworks (e.g., Pandas, SQL, BI tools).
- Familiarity with AI and machine learning data workflows.
