What we are looking for
Civis is seeking a leader who will inspire and develop a team that designs, builds, and supports the data infrastructure, architecture, transformations, and pipelines to make data science possible and sustainable for the long term. Your work will enable our organization to deliver exceptional data science solutions to our clients. You will do this by leveraging your data engineering expertise, developing a deep understanding of our clients' business contexts, and understanding how Civis data science software and methods can be brought to those problems.
- Develop a point of view on how Applied Data Engineering can best support our engagements and solutions.
- Manage and provide technical mentorship to the rest of the Applied Data Engineering team.
- Collaborate with our Engineering team to help them prioritize their work developing Civis' core ETL tools.
- Contribute to our most complex and important engagements by:
- Collaborating with clients and client-facing teams to define user requirements and database design specifications for our clients' needs.
- Transforming data based on business requirements.
- Applying data validation and/or software testing techniques to ensure data processes and pipelines are working properly.
- Creating pipelines that deliver data error-free and on-time with features such as logging, fault tolerance, notifications, and scalability.
- Documenting and training others on data pipelines.
- Work with software engineers and data scientists to help develop best practices, libraries, and internal trainings for applied data engineering, model deployment, and data architecture.
- Desire to define a vision for client-facing data engineering and to make it a reality.
- Demonstrated experience managing people and providing technical mentorship.
- Demonstrated experience collaborating with internal and client stakeholders.
- Passion for solving complex problems.
- Demonstrated experience overseeing the delivery of data engineering projects that:
- Use query and scripting languages to aggregate and transform data
- Include data quality processes, data quality checks, validations, data quality metrics, definition, and measurement
- Include features such as logging, fault tolerance, notifications, and scalability
- Involve large datasets
- Support of data analytics or data science applications.
- Strong communication and teamwork skills.
- US work authorization.