Principal Engineer (Data Services)

I. Job Summary

The Principal Software Engineer, within the Data Services (Corporate) team, is a principal-level technical role responsible for the design, development, and optimization of enterprise data solutions. This position focuses on building and maintaining scalable, reliable, and secure data systems that support business operations and analytics. The Principal Software Engineer undertakes highly complex projects requiring additional specialized technical expertise, applies advanced software engineering practices, works with modern data platforms (Snowflake, AWS, DBT, etc.), and ensures solutions follow enterprise standards.

 

While the primary focus is on engineering and solution delivery, the role also requires knowledge of data architecture principles and business analysis practices to effectively collaborate with architects, analysts, and stakeholders. This combination ensures that technical solutions are aligned with business needs and long-term data strategy.

II. Essential Duties and Responsibilities
 

• Build data pipelines required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Cloud Integration ETL/ELT tools including but not limited to Informatica, AWS Glue, DBT, etc., Cloud Data Warehouse including but not limited to Snowflake, Oracle, IBM PureData, SQL, Shell Scripting, Python, AWS technologies, GitHub, various scripting languages, data quality tools, and metadata management tools.
• Design, develop and document ETL/ELT, event-driven data integration architecture solutions.  Troubleshoot and tune complex SQL.
• Presents, communicates, and articulates technical processes effectively to all levels of the organization (including technical and non-technical audiences, Senior Leadership, VPs and the C-level executives).
• Contributes strategic vision and integrates a broad range of ideas regarding applications and software data development.
• Work with the Business Analysts, Data Analysts, Data Architects, BI Architects, Data Scientists, and Data Product Owners to establish an understanding of source data, determine data transformation and integration requirements to align engineering solutions with enterprise data strategies and technical standards
• Provide technical leadership in coding, testing, and code reviews; Mentors and fosters growth of peers and team members.
• Support data quality, governance, data observability, and compliance through development of frameworks, metadata, and standards.
• Apply engineering best practices to ensure performance, reliability, and scalability of enterprise data solutions.
• Works with customers and technical staff to resolve problems with software and responds to suggestions for improvements and enhancements.
• Participate in the creation of technical documentation, including data flows, integration specifications, and solution designs.
• Stay current with emerging technologies in data management and AI.  Recommend improvements to existing solutions.

III. Qualifications 

A. Required Qualifications 
 

• Education Requirement: Bachelor's Degree (accredited) in Computer Science, Data Science, MIS or similar area of study.
• Experience Requirement: Ten (10) years of previous experience required (in addition to education requirement).

B. Knowledge, Skill & Abilities 

• Expert level with data engineering expertise, including ETL, data warehouses, marts, and lake development
• Expert level experience working with SQL relational and noSQL databases, query authoring (SQL) as well as working familiarity with a variety of databases
• Expert level experience working with Cloud Datawarehouse like Snowflake, IBM PureData, Google BigQuery, Amazon Redshift
• Expert level experience working with AWS cloud services: ASW Glue, EC2, S3, Lambda, SQS, SNS, etc.
• Expert level experience working with with GitHub, CI/CD and its integration with the ETL tools for version control
• Expert level experience working with with Informatica PowerCenter, various scripting languages, SQL, querying tools
• Expert level experience working with with modern data management tools and platforms including Spark, NoSQL, APIs, Streaming, and other analytic data platforms    
• Proficient level experience in data observability    
• Proficient level experience in Agile/Scrum project management and product ownership
• Expert level in enterprise data management, integration patterns, and metadata practices
• Expert level to apply architecture and analysis knowledge when working with cross-functional teams
• Expert level strong problem-solving skills with attention to detail and