Data Engineer

We are seeking an experienced and visionary Senior Full-Stack Data Engineer to lead the architecture, development, and optimization of a next-generation data platform. This is a critical role for an individual with over 10 years of deep data engineering expertise, capable of driving technical direction, mentoring team members, and delivering high-impact solutions in a fast-paced project environment..

• Platform Strategy & Leadership 
  • Technical Direction: Define and champion the architectural roadmap and best practices for our end-to-end data pipelines, ensuring scalability, reliability, and security across the platform.
  • Team Mentorship & Project Velocity: Act as a primary technical mentor, guiding a team of engineers, conducting code reviews, and aggressively driving the project timeline to ensure rapid delivery of data products.
  • Stakeholder Collaboration: Partner with Data Scientists, Analysts, and business stakeholders to translate complex requirements into robust, production-ready data solutions.
  • Collaboration with Data Scientists and ML Engineers:  Data Accessibility, Support for Model Development, Data Quality Assurance
• Data Pipeline Development & Management
  • Ingestion & Transformation: Design, build, and optimize high-volume data ingestion and transformation jobs using tools like dbt Core, AWS Glue, ensuring data quality and integrity.
  • Workflow Orchestration: Develop and maintain sophisticated data pipelines using orchestrators such as Dagster, focusing on modularity and reusability.
  • Streaming & Real-time Integration: Implement and manage real-time data flows utilizing Confluent platforms or native AWS streaming services (e.g., Kinesis) for immediate data availability.
  • Data Security and Privacy: Data Anonymization, Compliance with Regulations
  • Be well versed with DataOps and DevOps fundamentals
• Assist and drive the Data Ecosystem Management & Monitoring
  • Open Table Formats & Management: Implement and maintain the Iceberg open table format, utilizing tools for efficient schema evolution and data management.
  • Compute Engine Optimization: Optimize query performance and cost efficiency across our primary compute engines: Snowflake, Amazon Redshift, and AWS Athena.
  • Observability & Monitoring: Integrate comprehensive monitoring and observability into all pipelines using Splunk to ensure high availability, rapidly identify bottlenecks, and troubleshoot production issues

• 10+ Years of hands-on, progressive experience in Data Engineering, Data Architecture, or a closely related Full-Stack Data role
• Deep conceptual understanding of core data engineering principles, ETL/ELT patterns, and metadata management 
• Proven track record of building and managing petabyte-scale data infrastructure in a cloud-native environment 
• Insurance industry experience preferred but not mandatory
• Tools:
  • Cloud Environment: AWS (S3, IAM, VPC, etc.)
  • Experience with Talend, dbt Core, Iceberg, AWS Glue Catalog, Snowflake, Redshift, Athena, Splunk, AWS streaming services, Git

Strong SQL, Pyspark and Python