Global Lead Data Engineer, Generation: You Employed

• 5+ of Data Engineering experience and have a demonstrated history of owning or making significant contributions to the evolution of data technologies
• Demonstrated leadership, management, and communication skills
• Demonstrated software development skills (e.g., Python, PySpark, R)
• Experienced with a cloud platform (AWS certified solution architect preferred)
• Familiar with CI/CD and DevOps concepts and practices (Experience with Circle CI preferred)
• Has working experience with Infrastructure as code (AWS CloudFormation or Terraform)
• Clear understanding of software engineering best practices and paradigms
• Working experience with Data Integration tools (PySpark, Matillion, FiveTran, Dbt, etc.)
• Familiarity with reporting tools (PowerBI, Looker, Tableau)
• Solid RDBMS and SQL skills paired with data modeling exposure (Snowflake core certified is preferred)
• Experience with structuring data, and data storage practices
• Experience with software development methodologies including Agile, Scrum
• Proven skills in SQL database technologies (Snowflake)
• Demonstrated project management skills
• Demonstrated stakeholder management experience
• Excellent written and verbal communication skills, willing to proactively engage other team members in fostering a strong collaborative team-oriented environment

Desirable

• Spearhead the modernization of our data and analytics platforms, enabling accurate analysis and real-time impact insights for our global organization
• This includes end-to-end data engineering from data pipeline to data modeling, data security, and generating data insights
• The Lead Data Engineer will report directly to our Global Director of Data and Impact with a dotted line to our Global Director of Digital
• Data requirements, stakeholder management, project management:
• Work closely with across organization to gain understanding of business processes, data definitions, data standardizations, and stakeholder usage
• Determine impacts to data warehouse structures and information flows due to changes in business or technical requirements
• Perform data quality analysis, report data quality issues and propose solutions for data quality management
• Create supporting documentation, guidance, and training
• Work effectively with both business leadership and technical teams
• Data engineering:
• Work effectively in AWS and Snowflake
• Influence key decisions on architecture and implementation of scalable data processing and analytics structure including data modeling (dimensional)
• Complete full life cycle of ETL/ELT development to address business needs or resolve issues including design, mappings, data transformations, scheduling, testing and scalable data models
• Develop ETL/ELT workflows, mappings, and data pipelines into target environments
• Develop data extraction and transmissions to external vendors
• Provide production support for data solutions
• Write test automation, conduct code reviews, and take end-to-end ownership of deployments to production
• Data architecture, quality, and security:
• Contribute to architectural decisions to support business processes
• Complete root cause analysis and contribute to remediation planning and implementation
• Prepare effort estimation including researching and estimating costs of software development, unit testing
• Define and build data processing pipelines to incorporate internal and external data sources
• Focus on performance, throughput, and latency, and drive these throughout our architecture
• Develop data governance in Snowflake, including column-level data security using secure views and dynamic data masking features
• Design and develop secure access to objects in Snowflake with Role-Based Access Control (RBAC)