Description
Collaborate with Data Engineer to design and implement scalable and reliable systems for ingestion, processing, and analysis of large disparate data sets from diverse sources.
Improve existing and create new data infrastructure components to better automate extraction, transformation, loading, and other data management processes.
Develop tools and applications to proactively measure, monitor, and improve data quality and consistency during loading and analysis processes.
Analyze and improve efficiency, reliability, and scalability of data infrastructure and processes.
Work with data team to define and promote best practices for data management and analysis, and to build and improve systems to implement and support these practices.
Maximizing the reliability and performance of services used in data infrastructure.

Requirements
4+ years of relevant work experience in data infrastructure and software engineering.
Strong scripting and development proficiency in two or more languages (Python preferred, experience in Airflow DAG is a plus).
Professional experience with multiple relational and non-relational database systems.
Desire and readiness to take on greater ownership of and responsibility for data infrastructure.
Professional experience retrieving, parsing, cleaning, and transforming data from multiple formats (e.g. XML, JSON, CSV, PDF, etc.) and delivery mechanisms (e.g. files, streams, APIs).
Experience with cloud infrastructure services, especially Google Cloud Platform.
Experience with development pipeline and continuous development with Git and its automation.
Experience with Kubernetes and has managed the deployment and services inside Kubernetes.
Familiarity with basic cloud networking.