Lead Data Engineer

• Lead and mentor a team of data engineers, setting technical direction, conducting code reviews, and driving engineering excellence across the data ecosystem.
• Own the end-to-end architecture, design, and implementation of scalable data platforms, pipelines, and data models, ensuring reliable and production-ready solutions.
• Define and lead data migration strategies from Snowflake/SSIS to Azure Databricks and DBT, including architecture decisions, cloud integration, data quality, and observability.
• Drive Infrastructure as Code with Terraform, CI/CD adoption, architectural standards, troubleshooting, and collaboration with data, product, architecture, and business stakeholders.

Technical Recruiting, Code Reviews, Mentoring, Ecosystems, Data Modeling, Migration Strategy, SQL Server Integration Services (SSIS), Microsoft Windows Azure, Data Migration, Snowflake Schema, * 8+ years of Data Engineering experience, including at least 2-3 years in a Lead or Staff-level role.

• Proven experience owning system design and leading implementations of large-scale data platforms.
• Strong hands-on expertise with Snowflake, Azure Databricks, DBT, Python, and advanced SQL.
• Experience with large-scale data transformations involving thousands of DBT models and pipelines processing terabytes of data daily.
• Deep understanding of data architecture, data modeling, distributed data systems, and ETL/ELT frameworks.
• Extensive experience with Terraform, Infrastructure as Code, CI/CD pipelines, and Git-based version control.
• Experience leading cloud-to-cloud data migration projects, preferably from AWS to Azure.
• Previous experience in insurance or financial services, with knowledge of domains such as claims, policies, underwriting, and risk.
• Proven experience leading and developing engineering teams, including mentoring, training, and managing different performance levels.
• Strong problem-solving, architectural decision-making, communication, and stakeholder management skills.

Preferred Skills:

• Experience implementing data governance, observability, and data quality frameworks.
• Strong knowledge of security and compliance standards within insurance or financial services.
• Experience with Kafka, Event Hubs, Delta Live Tables, or other real-time and streaming technologies.
• Familiarity with lakehouse architectures and cost/performance optimization strategies.
• Experience working in multi-cloud or hybrid cloud environments.