Principal Data Engineer

Summary

The Principal Data Engineer is responsible for transforming raw data from the organization's many data sources into formats that are more valuable and actionable for the enterprise. They will design efficient data models, build complex data pipelines, deliver analytically rich data sets, and construct solutions that facilitate self-service analytics. Additionally, the Principal Data Engineer defines and guides the domain-level data architecture, designs and manages the data environments that support analytics, mentors junior Data Engineers, leads data engineering projects, and consults on strategic initiatives.

What You'll Do

- Manage the domain's usage of enterprise data platforms and collaborate with platform teams on environment configuration, upgrades, access, and deployment needs. - Design, test, build, and manage complex, high-performing analytic solutions that support multi-user environments. - Design the domain-level data environment within established enterprise platforms and architectural standards, including data processing and storage systems used to capture, ingest, refine, secure, and deliver data across both on-premises and cloud environments. - Define domain-level data architecture, including conceptual, logical, and physical data models, and ensure alignment with enterprise data and governance standards. - Establish data product schemas, data contracts, and versioning approaches for the assigned business domain. - Ensure all data solutions incorporate governance-by-design principles such as lineage, data quality, metadata requirements, and privacy/security controls. - Coordinate technical interactions between information technology groups on environment and project matters. - Apply enterprise data engineering standards and patterns within the domain. - Provide consultation and support for junior data engineers and embedded (business area) data engineers and analysts. - Leverage techniques to build impactful data visualizations and enable insight generation. - Serve as the technical and architectural advisor for domain-specific data initiatives and ensure interoperability with enterprise data assets. - Handle other duties and projects assigned.

Additional Requirements and Qualifications

Technical Skills

- Languages: SQL (T-SQL, SnowSQL), Python - Platforms/Tools: Power BI, Snowflake, Microsoft Azure, Azure Data Factory, SSRS, Dataiku - Databases: SQL Server - Additional: Knowledge of SAP BI is a plus

Requirements

- Deep expertise with cloud-based data tools (Azure, Databricks, Synapse, Snowflake, or similar). - Strong proficiency with SQL and T-SQL in a SQL Server environment. - Expertise in designing and building data pipelines and data models. - Ability to create architecture diagrams, technical specifications, and solution designs. - Strong communication and collaboration skills with both technical and non-technical partners. - Experience coding ETL/ELT processes in Azure Data Factory. - Expertise in modern database technologies, data integration, reporting. - Solid understanding of data warehousing concepts and data modeling. - Strong critical thinking, problem-solving, and analytical skills. - Curiosity and passion around data, data visualization, and problem solving. - Creativity to determine novel solutions for real-world problems with quantitative data. - Experience mentoring other engineers and influencing engineering best practices.

Preferred Qualifications

- Advanced proficiency in Power BI. - Experience working on an Agile/Scrum team. - Knowledge of SRP water software systems and data.