Embrace Pet Insurance is seeking a dynamic and self-motivated Data Engineer to drive the evolution of our Data and Analytics capabilities. As a key contributor, you will play a pivotal role in the establishment of a cutting-edge foundation for Data Analytics, leveraging but not limited to modern Microsoft Azure Data technologies. We are looking for an individual who possesses a unique blend of creativity, independence, and strong collaboration skills.

Key Responsibilities:

• Take a leading role in designing and implementing a robust framework for Data Analytics u
• Collaborate with cross-functional teams to understand business requirements and translate them into efficient and scalable data solutions.
• Work independently to develop and optimize data pipelines, ensuring the integrity and quality of the data.
• Drive innovation by staying abreast of industry trends and incorporating best practices into our data engineering processes.

**This is a hybrid opportunity, at least 2 days onsite in the Cleveland, OH office**

Role Expectations:

Data Set Management:

• Develop, clean, organize, and optimize high quality datasets to meet the requirements of Data Science and Analytics teams, business and or project.

Data Warehouse and Pipeline Development:

• Design, implement, and maintain new data warehouses and pipelines
• Develop pipelines and data analytics solutions, leveraging structured, semi-structured, and unstructured data.
• Contribute to data warehousing initiatives, including design and implementation, ensuring adherence to best practices.

Cloud and Infrastructure Management:

• Maintain and expand the cloud and on prem infrastructure for efficient Extract, Transform, Load (ETL) processes.

Collaboration with Software Development:

• Collaborate closely with the Software Development team to align data structures and contribute to the design of new and existing applications.

Data Quality Assurance:

• Implement data quality checks to ensure accuracy and reliability of data; proactively identify and address potential issues.

Automation of Processes:

• Automate manual processes to enhance efficiency in data management and processing workflows.

Operational Efficiencies and Performance:

• Create and implement strategies to enhance operational efficiencies and mitigate performance issues within the data ecosystem.

Data and ETL Migration Planning:

• Develop comprehensive plans for data and ETL (Extract, Transform, Load) migrations, ensuring smooth transitions and minimal disruptions.

Mentorship and Knowledge Sharing:

• Mentor team members, share industry best practices, and contribute to the continuous learning culture within the data and analytics team.