Job Purpose and Impact

The Principal, Data Engineering job provides thought leadership in the execution of strategic plans related to design, development and maintenance of robust data systems. As a recognized subject matter expert in the field, this job leads the development of efficient processing and availability of data for analysis and reporting.

• Key Accountabilities
• DATA PIPELINES: Provides thought leadership on the design and development of data pipelines that facilitate the movement of data from various sources to internal databases.
• DATA INFRASTRUCTURE: Influences the construction and optimization of data infrastructure, providing appropriate data formats to ensure data readiness for analysis.
• DATA FORMATS: Examines and sees improvement opportunities for appropriate data formats to optimize data usability and accessibility across the organization.
• STAKEHOLDER MANAGEMENT: Cultivates positive relationships and partners to understand data needs and encourage alignment with organizational objectives.
• DATA SYSTEMS: Develops and guides the implementation of data products and solutions using advanced engineering and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable, and robust.
• SOLUTIONS DEVELOPMENT: Leads efforts to improve the development of technical products and solutions driving big data and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable, and robust.
• DATA FRAMEWORKS: Drives development standards and brings forward prototypes to test new data framework concepts and architecture patterns supporting efficient data processing and analysis and promoting standard methodologies in data management.
• AUTOMATED REPORTING SYSTEMS: Builds automated reporting systems that provide timely insights and facilitate data driven decision making.
• DATA MODELING: Provides thought leadership on data modeling and preparation of data in databases for use in various analytics tools and to develop data pipelines to move and improve data assets.

Qualifications:

• Minimum requirement of 6 years of relevant work experience. Typically reflects 10 years or more of relevant experience.

• PREFERRED QUALIFICATIONS

• ARCHITECTURAL LEADERSHIP: Defines long-term technical direction, establishes best practices, and ensures solutions are scalable, maintainable, and aligned with enterprise strategy.

• OPERATIONAL EXCELLENCE MINDSET: Champions reliability, observability, and performance, ensuring data systems meet high standards for availability and quality.

• STRATEGIC DECISION MAKING**:** Evaluates competing priorities such as speed, cost, risk, and flexibility to make sound technical and architectural decisions.

• CLOUD DATA PLATFORMS**:** deep expertise implementing cloud-based data warehouses, data lakes, and open table formats in large-scale production environments. Has hands-on experience with technologies such as Snowflake, AWS, and open lakehouse ecosystems.

• DATA INGESTION: Demonstrated proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet)

• DATA STREAMING: Experience developing data pipelines with streaming architectures and tools (Kafka, Flink).

• DATA TRANSFORMATION: Strong background with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.

• DEVOPS: extensive experience in DevOps practices, including code management, CI/CD, and deployment strategies.