Data Engineer

Job Overview: 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 15 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.. Key Responsibilities: • 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, or Flexter, ensuring data quality and integrity. • Workflow Orchestration: Develop and maintain sophisticated data pipelines using orchestrators such as Dagster or Talend, 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 like Upsolver (Talend Open Lakehouse) 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 Candidate Profile: • 15+ 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, including data modeling (e.g., Dimensional, Data Vault), 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