Principal Engineer – Software

The Principal Engineer, Data provides technical leadership across the design, development, modernization, and operation of enterprise data platforms and analytics solutions. This role is responsible for defining scalable data engineering patterns, leading complex data integration and transformation initiatives, and enabling reliable, governed, and high-performing data products that support business reporting, analytics, and advanced AI/ML use cases.

The role works closely with data analysts, data engineers, business stakeholders, architects, product owners, and technology partners to translate business needs into resilient data solutions. The Principal Engineer, Data serves as a senior technical expert and mentor, helping the team modernize from legacy reporting and ETL platforms toward cloud-native, automated, and analytics-ready data architecture.

• Provide technical feasibility analysis and solution evaluation for data and analytics initiatives based on business, operational, and regulatory needs.
• Lead the design and implementation of scalable data engineering solutions, including data pipelines, data models, data integrations, and analytics-ready data products.
• Provide technical leadership for data architecture, ETL/ELT design, code reviews, performance tuning, production support, and issue resolution.
• Guide modernization of legacy data and reporting platforms, including migration from traditional ETL and reporting tools to modern cloud data platforms and transformation frameworks.
• Design and oversee data pipelines using cloud-based technologies, Python, Spark, SQL, and related orchestration and transformation tools.
• Support enterprise analytics platforms, including data warehouse, reporting, semantic layer, and business intelligence environments.
• Partner with stakeholders to define and execute the technical roadmap for data engineering, analytics enablement, platform modernization, data quality, and automation.
• Establish and promote engineering standards, reusable patterns, coding guidelines, testing practices, data quality controls, and documentation expectations.
• Collaborate with architecture, security, infrastructure, governance, and business teams to ensure data solutions are secure, reliable, scalable, and aligned to enterprise standards.
• Mentor engineers and technical team members, supporting skill development in cloud data engineering, data modeling, analytics engineering, and modern data platform practices.
• Contribute to AI/ML enablement by ensuring data pipelines, curated datasets, and feature-ready data assets are reliable, governed, and suitable for advanced analytics and machine learning use cases.
• Stay informed about emerging data engineering, cloud, analytics, AI/ML, and automation trends to drive continuous improvement and technical innovation.

• Bachelor’s degree in computer science, information systems, data engineering, analytics, engineering, or equivalent training and experience.
• 12+ years of experience in software engineering, data engineering, analytics engineering, or enterprise data platform development.
• Proven experience designing, developing, and maintaining complex data solutions involving multiple systems, stakeholders, business domains, and production dependencies.
• Strong experience with Python, SQL, distributed data processing, and ETL/ELT development; experience with Spark or similar large-scale data processing frameworks strongly preferred.
• Experience with cloud-based data platforms and services, preferably AWS, including development, deployment, monitoring, and operational support of data solutions.
• Experience with enterprise data warehouses, data marts, reporting platforms, and business intelligence solutions; familiarity with platforms such as Redshift, Snowflake, SAP Business Objects Data Services, SAP Web Intelligence, or similar technologies preferred.
• Experience with modern data transformation and analytics engineering tools such as dbt or equivalent frameworks preferred.
• Strong understanding of data modeling, data warehousing, data quality, metadata management, data lineage, performance optimization, and production support practices.
• Work…