Architect

Job Summary
Serve as an architect for data and analytics solutions built on Databricks platforms designing scalable pipelines and frameworks that leverage Databricks SQL Databricks Workflows and PySpark. Apply extensive experience to optimize hybrid day shift delivery enable reliable insights for stakeholders and support innovation in insurance focused business environments.
Responsibilities
Design robust data architecture on Databricks platforms that aligns with enterprise standards and enables secure scalable analytics across hybrid work environments
Develop end to end data pipelines using PySpark and Databricks Workflows that transform complex raw data into curated datasets ready for business consumption
Optimize Databricks SQL queries and data models to improve performance reduce compute cost and ensure consistent response times for analytical workloads
Create reusable frameworks and patterns for ingestion transformation and data quality that can be adopted by engineering teams across multiple initiatives
Collaborate with product and business stakeholders to translate analytical and reporting needs into well defined data architecture and implementation plans
Integrate data from diverse source systems into unified models that support actuarial analysis financial reporting and operational monitoring for insurance focused solutions
Define standards for coding testing and documentation of PySpark jobs and Databricks assets to promote maintainability and long term platform resilience
Implement monitoring and alerting strategies on Databricks Workflows to ensure timely detection of pipeline failures and to support reliable data delivery in day shift operations
Partner with security and compliance teams to embed data governance privacy controls and audit readiness into all Databricks based solutions
Guide teams on efficient use of Databricks clusters storage and caching options to balance performance objectives with infrastructure cost management
Document architectural decisions data flows and dependency maps so that teams can easily understand solution design and support future enhancements
Coordinate with cross functional teams in a hybrid work model to plan releases manage dependencies and ensure smooth deployment of new data capabilities
Qualifications
Apply extensive experience in Databricks SQL to design analytical models reporting layers and interactive queries that support complex business insights
Leverage advanced proficiency in Databricks Workflows to orchestrate jobs manage dependencies and automate data operations with strong reliability
Utilize deep PySpark expertise to implement scalable transformations handle large data volumes and enforce data quality rules across the pipeline lifecycle
Draw on knowledge of life and annuities insurance to shape data models metrics and validations that reflect key policy claim and risk concepts when required
Demonstrate proven ability gained from twelve to sixteen years of experience in data engineering and architecture to handle complex enterprise grade solutions
Apply strong communication and collaboration capabilities to work effectively with business and technology partners in a hybrid non travel environment
Use experience with modern data platforms and cloud ecosystems to integrate Databricks solutions into broader enterprise architectures for long term value
Certifications Required
Preferred certifications include Databricks Certified Data Engineer Professional or Databricks Certified Data Engineer Associate.

Work arrangement
Hybrid