Principal Data Engineer

Job DescriptionJob Description The Opportunity Are you passionate about data, architecture, software development, and analytics? Do you bring deep experience with cloud technologies, data warehousing, data integration, data modeling, and API development? Do you believe in modern engineering practices, collaboration, and innovation to build the next generation of enterprise data and AI platforms? If so, we'd like to talk with you. our financial client is seeking a Principal Data Engineer to help design, build, and evolve modern data solutions within our Enterprise Data and AI organization. This role is ideal for a strong technical leader with hands-on experience delivering cloud-based data platforms, scalable data pipelines, and enterprise data solutions that support analytics, reporting, data science, and AI use cases. The ideal candidate will bring experience implementing data lakes, cloud data warehouses, and modern data engineering solutions, including data ingestion, transformation, standardization, preparation, data quality management, and structured and unstructured data processing. This role also requires experience with relational and NoSQL data technologies, large-scale data integration, API development, and enterprise data modeling, including Data Vault. Experience with modern cloud data platform technologies, data architecture, and software engineering practices is essential. Knowledge of AI/ML, data science, and LLM-related use cases is also a plus. Knowledge of the insurance business data domain is a significant advantage, particularly in supporting solutions that enable Insurance Data and Analytics capabilities. This role will be responsible for creating, maintaining, and optimizing enterprise data pipelines and related processes that support strategic analytics, machine learning, and AI-driven business capabilities across Insurance line of business. The selected candidate will help build and advance critical platform capabilities, including Insurance Data and Analytics solutions, data ingestion and transformation frameworks, governance-aligned data engineering processes, and trusted data foundations for analytics and AI. The ideal candidate will bring proven experience leading cloud data engineering initiatives, driving scalable solution design, and establishing strong engineering standards across complex enterprise environments. The Work Itself • Design, build, and optimize modern data pipelines and enterprise data solutions that support analytics, reporting, data science, and AI use cases. • Develop and implement cloud-based data engineering solutions, including data lakes, cloud data warehouses, and enterprise data platforms. • Design scalable data structures and support enterprise data storage using Data Vault modeling to enable flexible, auditable, and resilient data solutions. • Build and support data ingestion, transformation, preparation, and quality processes across structured and unstructured data sources. • Develop robust solutions for large-scale data integration, relational and NoSQL data processing, and API-based data services. • Enable trusted, high-quality, and accessible data for analytics, machine learning, and AI-driven business capabilities. • Partner with business and technology teams to define data requirements, transformation rules, integration needs, and solution designs that align to enterprise and line-of-business priorities. • Help advance Insurance Data and Analytics platform capabilities through scalable engineering, strong data foundations, and governance-aligned practices. • Establish and maintain data quality, validation, monitoring, metadata, and lineage processes to support reliable and well-managed enterprise data assets. • Apply modern software engineering methods and Agile practices to deliver scalable, reliable, and maintainable data solutions. • Promote engineering, operational, and design standards across data platforms and services. • Evaluate and recommend tools, technologies, and approaches that improve performance, delivery, and long-term supportability. • Collaborate across business and IT teams to solve complex technical challenges and deliver practical, scalable solutions. • Support enterprise data governance, data management, and data security requirements. • Provide technical leadership, mentor team members, and contribute to strong delivery outcomes through effective collaboration and sound engineering practices. The Skills You Bring • Bachelor's degree in Computer Science, Information Systems, or a related field; advanced degree preferred. • 10 years of progressive experience in data engineering, including deep expertise in analytics-focused data warehouse environments such as Snowflake. • Extensive hands-on experience designing and delivering cloud-based data solutions on AWS, including services such as S3, AWS CLI, Lambda, and DynamoDB. • Strong experience with modern data engineering and integration tools such as dbt, Qlik Replicate, InfoSphere DataStage, and CP4D. • Deep expertise in data modeling, including Data Vault, and in designing scalable, auditable, and resilient data structures that support enterprise analytics, reporting, and AI use cases. • Proven experience architecting and optimizing large-scale data ingestion, transformation, cleansing, standardization, and deduplication processes across complex data environments. • Strong programming and automation skills in Python and other scripting languages used to support enterprise data engineering solutions. • Demonstrated experience leading the design and implementation of enterprise data pipelines using Git, DevOps practices, and modern software engineering approaches. • Strong background in large-scale data integration, API development, and data migration across distributed systems and platforms. • Deep understanding of data governance, data management, metadata, lineage, and data security, with the ability to embed these practices into engineering solutions. • Proven ability to lead end-to-end solution delivery, working independently while providing technical direction across multiple initiatives and technologies. • Demonstrated success establishing and enforcing engineering, design, and operational standards across teams and platforms. • Ability to evaluate emerging tools, technologies, and architectural approaches, and recommend scalable solutions that improve performance, maintainability, and long-term supportability. • Proven ability to influence, advise, and partner effectively with senior business and technology stakeholders to identify strategic challenges, assess options, and recommend solutions. • Strong track record of mentoring engineers, promoting engineering excellence, and contributing to high-performing teams. • Extensive experience across the full software development lifecycle, including architecture, design, implementation, testing, deployment, and operational support. • Knowledge of the insurance business data domain is a major plus, including familiarity with insurance data concepts, business processes, and analytics use cases. • Ability to design trusted, high-quality data foundations that enable advanced analytics, machine learning, and AI-driven business capabilities. • Strong problem-solving, communication, and leadership skills, with the ability to translate complex technical concepts into practical business solutions. • Insurance Business data domain knowledge. Mandatory Skills: Desired Skills: The Skills You Bring • Bachelor's degree in Computer Science, Information Systems, or a related field; advanced degree preferred. • 10 years of progressive experience in data engineering, including deep expertise in analytics-focused data warehouse environments such as Snowflake. • Extensive hands-on experience designing and delivering cloud-based data solutions on AWS, including services such as S3, AWS CLI, Lambda, and DynamoDB. • Strong experience with modern data engineering and integration tools such as dbt, Qlik Replicate, InfoSphere DataStage, and CP4D. • Deep expertise in data modeling, including Data Vault, and in designing scalable, auditable, and resilient data structures that support enterprise analytics, reporting, and AI use cases. • Proven experience architecting and optimizing large-scale data ingestion, transformation, cleansing, standardization, and deduplication processes across complex data environments. • Strong programming and automation skills in Python and other scripting languages used to support enterprise data engineering solutions. • Demonstrated experience leading the design and implementation of enterprise data pipelines using Git, DevOps practices, and modern software engineering approaches. • Strong background in large-scale data integration, API development, and data migration across distributed systems and platforms. • Deep understanding of data governance, data management, metadata, lineage, and data security, with the ability to embed these practices into engineering solutions. • Proven ability to lead end-to-end solution delivery, working independently while providing technical direction across multiple initiatives and technologies. • Demonstrated success establishing and enforcing engineering, design, and operational standards across teams and platforms. • Ability to evaluate emerging tools, technologies, and architectural approaches, and recommend scalable solutions that improve performance, maintainability, and long-term supportability. • Proven ability to influence, advise, and partner effectively with senior business and technology stakeholders to identify strategic challenges, assess options, and recommend solutions. • Strong track record of mentoring engineers, promoting engineering excellence, and contributing to high-performing teams. • Extensive experience across the full software development lifecycle, including architecture, design, implementation, testing, deployment, and operational support. • Knowledge of the insurance business data domain is a major plus, including familiarity with insurance data concepts, business processes, and analytics use cases. • Ability to design trusted, high-quality data foundations that enable advanced analytics, machine learning, and AI-driven business capabilities. • Strong problem-solving, communication, and leadership skills, with the ability to translate complex technical concepts into practical business solutions. Insurance Business data domain knowledge.