Responsibilities: The Python Data Engineer / Automation Analyst will be responsible for designing, building, and maintaining robust data pipelines, analytics tools, and automation solutions to support actuarial and financial operations. This role involves working closely with actuarial analysts, data teams, and business stakeholders to streamline data flows, improve data quality, and reduce manual workload across the organization.
Key Responsibilities Data Engineering & ETL
Design, develop, and maintain scalable ETL processes using Python and related data frameworks.
Build pipelines to ingest, transform, and validate data from various internal and external sources.
Ensure data quality, completeness, and integrity across all stages of the pipeline.
Optimize existing ETL jobs for better performance and reliability.
Automation & Process Improvement
Identify manual, repetitive processes within actuarial and analytics teams and automate them using Python, APIs, or workflow orchestration tools.
Develop scripts and automation tools to streamline reporting, data validation, and data preparation tasks.
Implement monitoring, logging, and error-handling frameworks for automated workflows.
Actuarial Analytics Support
Support actuarial teams by preparing data for modeling, reserving, pricing, forecasting, and financial reporting.
Build utilities to help analysts efficiently analyze large datasets.
Assist with the production of actuarial models or dashboards where needed.
Collaboration & Stakeholder Support
Work with actuaries, data scientists, and business users to understand analytical requirements and translate them into technical solutions.
Document data processes, automation logic, and technical designs for internal reference.
Provide technical troubleshooting and support for data-related or automation issues.
Technical Standards & Best Practices
Follow best practices for Python development, version control, testing, and deployment.
Contribute to improving data engineering standards, coding practices, and team workflows.
Evaluate new tools, libraries, and methods to enhance data processing and automation efficiency.
