Technical Lead

Job Summary

Responsibilities

• Drive end to end design and implementation of scalable data pipelines using Azure Data Factory and Databricks to support enterprise analytics and reporting needs across the organization
• Lead technical solutioning for complex data integration scenarios by defining patterns for ingestion transformation and orchestration that ensure reliability performance and maintainability
• Coordinate hybrid model delivery activities by planning onsite and remote work schedules that optimize collaboration with cross functional partners and maintain consistent progress on critical milestones
• Provide technical guidance to data engineers and developers by reviewing code design artifacts and deployment approaches to uphold high standards of quality security and operational stability
• Collaborate with business and product stakeholders by translating analytical and reporting requirements into well structured Azure based data solutions that deliver measurable value to customers and internal teams
• Optimize Databricks workloads by tuning clusters jobs and queries to improve cost efficiency execution speed and resource utilization while enforcing best practices for workspace organization and governance
• Implement robust monitoring logging and alerting for Azure Data Factory and Databricks jobs by leveraging native tools and dashboards to proactively identify issues and reduce downtime
• Ensure data quality and consistency across data pipelines by designing validation rules reconciliation checks and error handling strategies that strengthen trust in curated datasets and downstream analytics
• Coordinate secure data access and compliance alignment by working with security and risk partners to apply appropriate controls masking and role based permissions across data platforms
• Support project planning and estimation activities by providing realistic effort assessments for data engineering tasks enabling accurate timelines and resource allocation for delivery teams
• Partner with architecture and infrastructure groups by aligning solutions with enterprise standards reference architectures and cloud governance guidelines to maintain long term sustainability
• Contribute to continuous improvement initiatives by capturing lessons learned researching emerging Azure data services and proposing enhancements that increase productivity and innovation across projects
• Engage with domain experts in life and annuities insurance when available by shaping data models metrics and analytics that reflect key business processes and improve decision making for policy and claims outcomes

Qualifications

• Demonstrate extensive hands on expertise with Azure Data Factory including pipeline development data flow configurations and integration runtime management for complex enterprise workloads
• Apply strong Databricks knowledge by building performant notebooks jobs and workflows that leverage Spark based processing for batch and near real time data transformation scenarios
• Bring proven experience in designing cloud data architectures that include storage choices compute strategies and integration patterns aligned with organizational standards and performance expectations
• Utilize solid data engineering fundamentals such as modeling ETL design optimization and testing to create resilient data solutions that are easy to maintain and extend over time
• Leverage any background in life and annuities insurance to interpret business concepts such as policy lifecycle underwriting claims and actuarial analysis when designing data structures and analytics outputs
• Communicate effectively with technical and nontechnical partners by explaining solution approaches dependencies and risks in clear language that supports informed decisions and collaborative planning
• Operate comfortably in a hybrid work environment by using collaboration tools disciplined documentation and regular touchpoints to maintain transparency and alignment across onsite and remote participants

Certifications Required