• 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.

• Architect the Data Platform – Own the end-to-end design of our data infrastructure. • Make the foundational calls on pipeline architecture, data modeling patterns, and platform evolution across Snowflake, dbt, Airflow, and Terraform. • Define Engineering Standards – Establish and enforce practices around data quality, testing, observability, and deployment that the rest of the team builds on. • Enable Data-Driven Decisions at Scale – Design semantic layers and data models complex enough to support underwriting, finance, and executive strategy. • Drive Data Governance – Own the governance posture: data contracts, SLAs, lineage, and documentation. • Shape the ML and AI Foundation – Partner with data science and engineering leadership to ensure the platform supports advanced analytics, ML pipelines, and AI initiatives. • Elevate the Team – Mentor engineers, conduct rigorous code and design reviews, and actively close skill gaps. • Partner at the Leadership Level – Engage directly with actuarial, underwriting, finance, and product leaders to translate business complexity into technical roadmap.