• Manage, mentor, and grow a 10-person data engineering team and a 3-person AI/ML team; own headcount planning and hiring across both • Set a unified roadmap where data infrastructure and AI/ML development reinforce each other • Build a culture of technical rigor, ownership, and delivery • Lead development of ML models using proprietary insurance data: risk scoring, pricing signals, anomaly detection, loss prediction • Own LLM integration strategy from prompt engineering and RAG pipelines to fine-tuning and agentic workflows • Drive AI automation across operations: underwriting intake, document processing, triage, internal tooling • Partner with the CTO on enterprise AI platform decisions: tooling, deployment infrastructure, model governance • Build the evaluation, monitoring, and feedback loops that turn experiments into production systems • Set architectural standards for pipelines, data modeling, and platform infrastructure • Own reliability, observability, and data quality across Snowflake, dbt, Airflow, and Terraform • Build semantic layers and data models that serve underwriting, pricing, finance, and executive reporting • Establish data governance, quality frameworks, and documentation standards that scale • Collaborate with actuaries, underwriters, engineers, and product leaders to translate business needs into AI and data solutions • Operate as a senior technical voice in planning, roadmap, and strategy discussions

• Design, develop, and maintain scalable data pipelines and data platforms • Build and optimize ETL/ELT processes for large-scale data processing • Work with Databricks to develop and manage data workflows and analytics solutions • Implement data processing solutions using Apache Spark and modern data engineering practices • Collaborate with data scientists, analysts, and business stakeholders to enable data-driven insights • Ensure data quality, reliability, and performance of data pipelines • Contribute to architecture decisions and data platform improvements • Mentor junior engineers and promote best practices in data engineering

• Design, develop, and optimize database schemas, stored procedures, functions, triggers, and views in SQL Server and other RDBMS platforms. • Provide production database support, including incident response, troubleshooting, root-cause analysis, and resolution of performance and availability issues. • Plan and execute data patching, hotfixes, and schema changes across development, test, and production environments with minimal downtime. • Perform performance tuning, query optimization, indexing strategy, and execution-plan analysis. • Administer database instances: installation, configuration, upgrades, patching, backup and recovery, and disaster recovery planning. • Manage and support databases in cloud environments — Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure VMs, or equivalent services on other cloud platforms. • Implement and maintain security controls, access management, encryption, and compliance requirements. • Monitor database health, capacity, and resource utilization; set up alerting and proactive maintenance. • Support migrations between on-premises and cloud, and between database versions. • Collaborate with development teams on data modeling, release management, and deployment automation. • Maintain documentation for configurations, procedures, and runbooks.

• Support the development and maintenance of data pipelines, ingestion processes, and data transformations. • Create and maintain SQL queries, Python scripts, and Spark-based workloads used for data processing and analytics. • Assist in troubleshooting pipeline failures, data quality issues, and operational incidents. • Work with senior engineers to implement schema mappings, transformation logic, and data validation rules. • Ensure datasets meet expected schemas, data contracts, and quality standards. • Support metadata management, dataset documentation, and lineage activities. • Assist in maintaining data classification information according to company standards. • Help automate repetitive operational and data management tasks to improve efficiency and reliability. • Contribute to monitoring, alerting, and operational support for data pipelines and workflows. • Participate in testing activities, including unit tests, transformation validation, and data quality checks. • Follow established engineering standards, coding practices, and team development patterns. • Learn and apply security, privacy, and compliance requirements when handling sensitive or regulated data. • Collaborate with Data Governance, Security, and Compliance teams when required. • Contribute to continuous improvement initiatives focused on data trust, reliability, and operational excellence.