Principal Data Engineer

Seeking a Principal Data Engineer to join its Enterprise Data and AI organization. This role is ideal for a senior technical leader with strong hands-on experience in cloud data platforms, data engineering, data architecture, data integration, data modeling, and API development. The selected candidate will design, build, and evolve modern enterprise data solutions supporting analytics, reporting, data science, machine learning, and AI initiatives, with a particular focus on Insurance Data and Analytics. The ideal candidate will have experience with data lakes, cloud data warehouses, modern data engineering, Data Vault modeling, large-scale data integration, structured and unstructured data, relational and NoSQL technologies, and enterprise data pipelines., * Design, build, and optimize modern data pipelines and enterprise data solutions.

• Develop cloud-based data engineering solutions, including data lakes, cloud data warehouses, and enterprise data platforms.
• Design scalable and auditable data structures using Data Vault modeling.
• Build and support data ingestion, transformation, cleansing, standardization, deduplication, and data quality processes.
• Work with both structured and unstructured data sources.
• Develop solutions for large-scale data integration, relational and NoSQL data processing, and API-based data services.
• Enable trusted, high-quality data for analytics, machine learning, and AI use cases.
• Partner with business and technology teams to define data requirements, transformation rules, integration needs, and solution designs.
• Establish data quality, validation, monitoring, metadata, and lineage processes.
• Apply modern software engineering and Agile practices to build scalable and maintainable solutions.
• Establish and promote engineering, operational, and design standards across data platforms.
• Evaluate and recommend tools, technologies, and architectural approaches.
• Support enterprise data governance, data management, and data security requirements.
• Provide technical leadership and mentorship to engineers.
• Lead end-to-end solution delivery across architecture, design, development, testing, deployment, and operational support.
• Help advance Insurance Data and Analytics platform capabilities.

• 10+ years of progressive experience in Data Engineering.
• Strong experience working with analytics-focused data warehouse environments, particularly Snowflake.
• Extensive hands-on experience designing and delivering AWS cloud-based data solutions.
• Experience with AWS services such as:
• S3
• AWS CLI
• Lambda
• DynamoDB

Data Engineering & Integration

• Strong experience with modern data engineering and integration tools, including:
• dbt
• Qlik Replicate
• InfoSphere DataStage
• CP4D
• Deep expertise in Data Modeling and Data Vault.
• Experience designing scalable, auditable, and resilient enterprise data structures.
• Strong experience with large-scale:
• Data ingestion
• Data transformation
• Data cleansing
• Data standardization
• Data deduplication
• Data migration
• Data integration

Programming & Development

• Strong Python programming and automation skills.
• Experience with other scripting languages used in enterprise data engineering.
• Experience developing enterprise data pipelines using Git, DevOps, and modern software engineering practices.
• Strong experience with API development.

Data Governance & Architecture

• Strong understanding of:
• Data governance
• Data management
• Metadata
• Data lineage
• Data security
• Data quality
• Ability to embed governance and security practices into data engineering solutions.
• Experience establishing engineering, design, and operational standards.
• Ability to evaluate emerging technologies and recommend scalable architectural solutions.

Leadership

• Proven ability to lead end-to-end technical solutions independently.
• Experience providing technical direction across multiple initiatives.
• Strong stakeholder management and communication skills.
• Ability to translate complex technical concepts into practical business solutions.
• Experience mentoring engineers and promoting engineering excellence., * Bachelor’s degree in Computer Science, Information Systems, or a related field; advanced degree preferred.
• Experience with Insurance business data domains.
• Understanding of insurance data concepts, business processes, and analytics use cases.
• Experience supporting AI/ML, data science, and LLM-related use cases.
• Experience building trusted data foundations for advanced analytics, machine learning, and AI.
• Strong knowledge of modern cloud data architecture and enterprise data platforms.