Sr. Azure Data Architect – Databricks
Overview :
Location: REMOTE Hybrid
Key Responsibilities
- Design and implement end-to-end data architecture / data ecosystem using Azure-native services and Databricks
- Build scalable, high-performance data platforms and robust data frameworks
- Partner with stakeholders to translate business requirements into reliable data solutions
- Implement data governance principles using Unity Catalog
- Create and implement data ingestion and data integration patterns using Medallion Architecture
- Apply best practices in data engineering
- Develop PySpark notebooks
- Create procedures for advanced data transformation
- Handle performance tuning in Databricks and Azure SQL DBs
- Architect end-to-end data solutions supporting:
- Yield improvement
- Process control
- Predictive maintenance
- Operational efficiency
- Yield improvement
- Ensure strict governance, security, scalability, and compliance across the platform
Required Experience
- 10+ years of overall experience and 5+ years of architecture experience in data architecture / data engineering roles
- Strong hands-on experience with major enterprise data platforms
- Extensive experience with Azure-native services including:
- Azure Data Lake Storage (ADLS)
- Azure Data Factory (ADF)
- Azure Synapse Analytics
- Azure SQL
- Azure Data Lake Storage (ADLS)
- Strong hands-on experience with Azure Databricks
Required Databricks Expertise
Should have worked with the latest Databricks capabilities including:
- Unity Catalog:
- Catalog and schema design
- Permissions and security
- Managed and external tables
- Lineage and governance
- Catalog and schema design
- Delta Live Tables (DLT):
- Reliable pipeline development
- CDC implementation
- Data transformations
- Data quality controls
- Scaling and performance tuning
- Reliable pipeline development
- Other modern Databricks capabilities and best practices
Data Ingestion & ELT Experience
- Experience with data ingestion tools such as:
- Fivetran
- HVR or equivalent CDC / replication tools
- Fivetran
- Experience with ELT tools such as:
- DBT
- DBT
- Familiarity with:
- Real-time / streaming data
- Batch processing
- Sensor / OT / ET data flows
- Real-time / streaming data
Requirements
Technical Skills
- Strong expertise in data ingestion, transformation, and orchestration frameworks
- Hands-on experience building insurance-specific data models
- Proficiency in Python / PySpark and Spark SQL
- Strong understanding of streaming vs batch processing
- Experience with Spark job performance tuning
- Ability to optimize:
- Data storage formats
- Partitioning strategies
- Schema design
- Data storage formats
- Experience working with:
- Object stores
- Data lakes
- Cloud compute scaling
- Databricks cluster management
- Object stores
Governance, Security & Operations
- Deep understanding of:
- Data governance
- Security and compliance
- RBAC and attribute-based access control
- Encryption and audit logs
- Data privacy requirements
- Data governance
- Experience with operational excellence practices:
- Monitoring and observability
- Reliability and metrics
- Backup, failover, and disaster recovery strategies
- Monitoring and observability
Preferred Qualifications
- Prior experience working on Insurance P&C cloud-based data and analytics projects
- Strong analytical and problem-solving skills
- Experience defining and enforcing best practices for Databricks workloads and Spark-based pipelines
- Solid understanding of distributed data processing concepts
- Strong communication and stakeholder management skills
- Ability to document architecture decisions, trade-offs, and technical recommendations