• This role requires a leader with a strong technical background, a proven track record in data engineering, and people management experience
• The ideal candidate will have over 10 years of experience in the field, with a deep understanding of data architectures, ETL processes, documentation processes, data warehousing, and people management
• Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field
• 10+ years of experience in data engineering, with at least 3 years at a manager level
• Proven experience with data architecture, data modeling, and database design
• Strong proficiency in SQL, Python, and other programming languages relevant to data engineering
• Extensive experience with ETL tools (e.g., Apache Airflow, Talend, Informatica)
• In-depth knowledge of data warehousing solutions (e.g., Postgres, Snowflake, Redshift, BigQuery)
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and related services
• Excellent problem-solving skills and attention to detail
• Strong project management skills and the ability to manage multiple priorities in the face of ambiguity
• Excellent communication and interpersonal skills
• Experience with big data technologies (e.g., Hadoop, Spark) is a plus

Desirable

• The Data Engineering Manager will oversee the design, development, and maintenance of our data lake, as well as manage our BIE team
• Design and implement robust, scalable, and efficient data pipelines and workflows
• Collaborate with Data Scientists, Business Intelligence Engineers, Data Engineers, and other stakeholders to understand data needs and deliver high-quality data solutions
• Ensure data quality, integrity, and security across all data systems
• Develop and maintain documentation related to data architecture, processes, and standards
• Stay current with industry trends and best practices, and integrate new technologies and methodologies as appropriate
• Oversee the deployment and management of data warehousing solutions
• Implement and manage ETL processes, ensuring optimal performance and reliability
• Drive continuous improvement in data engineering practices, tools, and processes
• Manage project timelines and resources effectively
• Communicate effectively with technical and non-technical stakeholders, translating complex technical concepts into business terms
• People management