Data Engineer

As an intermediate level Data Engineer, you will use various methods to transform raw data into useful data systems. You will create complex algorithms for the purpose of aligning data systems with business goals. You will manage projects within larger projects.

Responsibilities:

Build and maintain data systems and pipelines to support increases in data volume and complexity

Contribute towards the development, construction, and maintenance of data models within data warehouses using dimensional modeling

Collaborate with analytics and business teams to improve data models in order to increase data accessibility and foster data driven decision making in the organization

Build and maintain scalable data pipelines for both batch and stream processing in a cloud-computing environment.

Create and optimize database architecture by trading off storage and computation to achieve low cost and high performance.

Interact face-to-face with business stakeholders, develop cooperative relationships, and acquire domain knowledge of the business.

Proactively automate manual processes throughout the business for higher efficiency, robustness, and speed.

Enforce production standards and governance best practices in the management of enterprise-level data, metrics, and reports.

May include other duties as assigned

Qualifications

Minimum Education, Licensure and Professional Certification requirements:

Bachelor's degree in Computer Science, Statistics, or similar field

Minimum Experience required:
2+ years experience in relevant field

Required Skills:

Excellent communication and inter-personal skills.

Versatile and quick learner with ability to pick up any new skills necessary to get the job done.

Demonstrated strength in data modeling, ETL development, data warehousing, data pipeline and data lake creation.

Extensive experience with cloud infrastructure and tools for AWS and GCP.

Demonstrable proficiency in Python development and advanced SQL querying.

Strong grasp of business elements and ability to convert requirements into database models and full-data pipeline systems.

Experience with visualization and reporting tools, such as Looker and Tableau.

Experience with cloud data warehouses such as Snowflake

Experience building data lakes in the cloud