- Define and own the enterprise data platform strategy, ensuring scalability, performance, and cloud optimization
- Oversee migration from legacy Azure SQL Server systems to an Azure Databricks Lakehouse using medallion architecture
- Lead and mentor data engineers and guide the onsite and remote data engineering team
- Partner with enterprise data and solution architects on data models, pipelines, and platform design
- Transform a fragmented data ecosystem into a single source of truth for insurance domains
- Drive cloud-native engineering best practices, including ETL/ELT optimization, CI/CD, DevOps, cost efficiency, and observability
- Ensure data availability, scalability, and reliability for analytics, reporting, and digital initiatives
- Collaborate with analytics and data science teams to enable advanced analytics, AI/ML, and self-service data access
- Prioritize and manage the enterprise data portfolio and align investments with business value and strategic initiatives
- Manage budget, resourcing, and vendor relationships
- Evolve the platform roadmap based on emerging technologies such as Databricks, streaming, and AI/ML
- Own the Databricks platform and guide business users on Lakehouse adoption
- Report to the Chief Data & Analytics Officer
Requirements
- Expertise with Azure Data Services, including Data Lake, Data Factory, Synapse, Event Hub, and Key Vault
- Ability to lead teams while defining enterprise platform strategy and technical roadmap
- Excellent communication and leadership skills to influence technical and business stakeholders
- Familiarity with DAMA/DMBOK practices and standards
- Familiarity with streaming/real-time ingestion, including Kafka and Event Hub, required
- 10+ years of experience in data engineering, architecture, or platform leadership
- At least 5 years of team management experience
- Insurance or financial services experience required
- Proven success leading large-scale data modernization programs in Azure or similar cloud ecosystems using Databricks or Snowflake
- Hands-on experience with Databricks Lakehouse, PySpark, Delta Lake, and Unity Catalog
- Strong background in data modeling, ETL/ELT frameworks, and data warehousing
- Experience with compliance regulations and handling PHI/PII data
- Exposure to AI/ML and data science workloads in Databricks or Snowflake
- Experience with DevOps and automation frameworks, including Azure DevOps, Terraform, and GitHub Actions
- Bachelor's degree in computer science, business/data analytics, management information systems, information technology, or related field; combination of education and/or relevant work experience may be accepted in lieu of degree
Demonstrates expertise in defining and executing enterprise data platform strategies, with a strong focus on Azure Data Services, Databricks Lakehouse, and data modernization. Proven ability to lead teams, manage budgets, and drive cloud-native engineering best practices while ensuring data availability and compliance.
Highest-signal resume keywords
- Azure Data Services
- Databricks Lakehouse
- Data Engineering Leadership
- ETL/ELT Optimization
- Insurance Domain Experience
ATS Optimization Keywords
Hard Skills
- Data Modeling
- PySpark
- Delta Lake
- Unity Catalog
- CI/CD
- DevOps
- ETL Frameworks
- Data Warehousing
- Streaming Ingestion
- Compliance Regulations
Soft Skills
- Leadership
- Communication
- Team Management
- Stakeholder Influence
- Collaboration
Industry Keywords
- DAMA/DMBOK
- Data Modernization
- PHI/PII Data
- Financial Services
- Insurance
Tools & Technologies
- Azure Databricks
- Azure SQL Server
- Azure Data Factory
- Azure Synapse
- Event Hub
- Azure DevOps
- Terraform
- GitHub Actions