Data Engineer, Principal

About the role

The Data Services team is responsible for technical design and end-to-end delivery of complex data-driven solutions and data products for the enterprise. The Data Engineer, Principal will report to the Sr Manager, Data Solutions / Director. In this role, you will partner with Enterprise Architects, Portfolio, Analytics, and Data Engineering teams for designing technical solutions and building data products to meet enterprise data needs. You will be responsible for driving the data product by designing and implementing cloud data lakes, data warehouses, and data marts.

Responsibilities

• Lead the design, development, and implementation of scalable data pipelines supporting enterprise data lakes, data warehouses, and data marts.
• Engineer robust ELT/ETL solutions that ingest, process, and curate structured and semi-structured data from diverse internal and external sources.
• Apply advanced data modeling techniques (including Data Vault 2.0, dimensional, and domain-oriented models) to support analytics and data products.
• Persistently partner with Solution Design, Architecture, and Product teams to ensure technical designs are implemented accurately, efficiently, and securely.
• Build and optimize data solutions on cloud platforms (e.g., Snowflake, Databricks, Synapse) with a focus on performance, scalability, reliability, and cost efficiency.
• Implement data quality, validation, observability, lineage, and governance controls embedded directly into data pipelines.
• Champion and apply DevOps and DataOps best practices, including CI/CD, automated testing, infrastructure as code, monitoring, and alerting.
• Provide hands-on technical leadership and mentorship to senior and mid-level data engineers, promoting engineering standards and best practices.
• Collaborate using agile methodologies to plan work, refine technical stories, and deliver iteratively with predictable outcomes.
• Identify performance bottlenecks, reliability risks, and optimization opportunities across data platforms and workflows.
• Support integration of AI/ML-ready data assets, ensuring data is trustworthy, well-modeled, and accessible for advanced analytics use cases.
• Act as a technical thought leader, advocating for modern data engineering patterns, tools, and practices aligned to enterprise strategy.

Qualifications

• Requires a bachelor's degree or equivalent experience.
• Requires a minimum of 10 years of relevant data engineering experience.
• Experience supporting enterprise AI/ML, advanced analytics, and data product ecosystems.
• Expertise in Data Vault 2.0, dimensional modeling, Lakehouse architecture, or domain-driven data design.
• Demonstrated ability to influence enterprise architecture decisions and mentor senior engineering talent.
• Expert in understanding data management practices, data modelling (Data Vault 2.0), master data management, data integration, data architecture, data virtualization, data warehousing, data privacy and security.
• Demonstrated enthusiasm for AI and emerging technologies, with solid understanding of AI/ML concepts and hands-on experience applying AI-driven solutions in enterprise environments.
• Hands-on experience with SQL, Python, DBT Cloud and DBT core.
• Expert in one or more of NoSQL and database appliances and platforms (Snowflake, Synapse, Databricks).
• Proven ability to design and scale large-volume, high-performance data platforms leveraging parallel and distributed architectures.
• Advanced knowledge of CI/CD, source control, and agile delivery tooling (e.g., Git, Bitbucket, Jira).
• Experience in healthcare, regulated environments, or large enterprises is strongly preferred.