Staff Data Engineer

Inspiren

14d•$200,000 - $225,000•Remote

About The Position

We are seeking a highly skilled Staff Data Engineer to own and evolve our data pipelines and infrastructure. You will drive innovation, ensure the integration of cutting-edge technologies, and deliver data-driven solutions that meet the highest standards of quality and performance of Inspiren’s data systems for external and internal consumers.

Requirements

Bachelor's or Master's degree in Computer Science, or a related engineering field.
Fluency in building and maintaining ETL processes.
Outstanding analytical skills and the ability to address problems in real-world settings
A demonstrated ability to work in a team, with excellent skills-sharing capabilities.
8+ years of full stack or backend development experience
Expertise in modern ETL technologies and building and supporting data pipelines at scale
Proven experience in evaluating and optimizing data architectures to increase performance, data discovery, and reduce cost.
Proven experience with cloud-based data engineering pipeline design at scale. AWS and Databricks experience are plusses.
Proven proficiency in one or more programming languages such as Python or Java, as well as SQL.
Well-versed in the development lifecycle and software engineering best practices.
Excellent verbal and written communication skills, with the ability to convey complex ideas clearly.
Comfortable working in a fast-paced, dynamic environment and adapting to changing priorities.

Nice To Haves

Start-up experience is a plus.
Health-tech experience is a plus.

Responsibilities

Collaborate with engineering, data science, ML, and product analytics teams to develop data models and pipelines for customer-facing applications, research, reporting and machine learning.
Develop, implement and optimize ETL processes for ingesting, processing and transforming large volumes of structured and unstructured data into our data ecosystem
Optimize data models to support efficient data storage and retrieval processes for performance and scalability.
Evaluate and implement a variety of data storage solutions, including RDS, NoSQL, data lakes and cloud storage services.
Work in close partnership with Platform Engineering to influence the direction and needs of the data platform.