Description:
The Data Engineer is responsible for building, maintaining, expanding, and optimizing data pipelines, data products and data practices, and then moving these data pipelines effectively into production for key analytics consumers like Actuarial, Data Analysts and ML Engineers to support advanced analytics use cases across the enterprise.
Job Responsibilities
- Understanding and translating business and functional needs into machine learning problem statements (Senior Level)
- Architecting data pipelines (Senior Level)
- Creating and maintaining data pipelines
- Discovering new data acquisition opportunities
- Automation of the most-common, repeatable, and tedious data preparation and integration tasks
- Collaboration with business partners, Analytics Exploration and Operations teams in refining their data requirements for various analytics initiatives and their data consumption requirements
- Renovating the data management infrastructure to drive automation in data integration and management (Senior Level)
- Ensuring that the data users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives (Senior Level)
- Promoting the available data and analytics capabilities and expertise to business leaders and educate them in leveraging these capabilities in achieving their business goals
- Performs other duties as assigned
Qualifications
- More than 6 years (10 years for Senior Level) of experience in data modeling, data management and data engineering. Strong experience with various Data Management architectures like Data Warehouse, Data Lake, Data Hub and the supporting processes like Data Integration, Governance, Metadata Management
- 2+ years of experience as a mentor, tech lead OR leading an engineering team (Senior Level)
- A bachelor's degree in data management, information systems, software engineering, computer science or a related quantitative field or equivalent work experience is required
- Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management
- Proficiency in SQL and NoSQL
- Proficiency in Python, Linux, Bash
- Proficiency with AWS technologies or equivalent cloud services including data tooling
- Proficiency with AWS CloudFormation or equivalent infrastructure as code
- Proficiency with Git, CI/CD
- Proficiency with big data tools such as Hadoop and Spark is an asset (Senior Level)
- Previous experience with technologies such as DataBricks, Snowflake, or a cloud ETL stack is an asset
- Knowledge of Guidewire Gosu is an asset
- Strong ability to support and work with cross-functional teams in a dynamic business environment
- Required to be highly creative and collaborative
- Required exceptional communication skills and ability to translate technical concepts into appropriate language for all stakeholders (Senior Level)
- Has good judgment, a sense of urgency
- Commitment to high standards of ethics, regulatory compliance, customer service and business integrity