Mission

We create technology with heart for the health of every person in the world.

About Buoy Health

Buoy builds a digital health tool that helps people – from the moment they get sick – start their health care on the right foot. Started by a team of doctors and computer scientists working at the Harvard Innovation Laboratory in Boston MA, Buoy was developed in direct response to the downward spiral we've all faced when we attempt to self-diagnose our symptoms online. Buoy leverages artificial intelligence – powered by advanced machine learning and proprietary granular data - to resemble an exchange you would have with your favorite doctor – to provide consumers with a real-time, accurate analysis of their symptoms and help them easily and quickly embark on the right path to getting better. Buoy is based in Boston and was founded in 2014.

About the role

The Analytics Engineer will work across different areas including data engineering, analytics, and business intelligence. The Analytics Engineer is responsible for managing data warehousing environments, building ETL/ELT pipelines and job orchestration frameworks, constructing complex business logic and data models, supporting business intelligence, and ensuring data quality. The charter of the data engineering and analytics team is to promote a data driven culture throughout the company, and to make high quality data broadly accessible and easy to work with for analysis, data science, and machine learning. The analytics engineer can expect to work closely with business analysts, data scientists, machine learning engineers, and developers to help build our data warehouse, ETL pipelines, and data models. We are currently building our data analytics ecosystem from the ground up, and our company and datasets are growing rapidly - so the analytics engineer will also have the opportunity to inform the design, implementation, and best practices or this system.

Responsibilities

-- Support cloud-based data warehousing environments, data processing pipelines, and data models that support a variety of business needs

-- Support a variety of data processing pipelines, integrate new data sources into our data warehouse, and create jobs to load, transform, and QA vital datasets

-- Work with data scientists, analysts, and developers in the product development process to ensure that newly designed data models meet analytics requirements and follow best practices

-- Embed business logic and data models into business intelligence tools for easy consumption by end users

About you

-- 2+ years of relevant experience with data engineering and warehousing technologies like Snowflake, Amazon Redshift, S3, Athena, EMR, and Hadoop/Hive/Spark

-- Proficient in SQL including one or more warehouses or relational databases like Snowflake, MySQL, MariaDB, Oracle, Postgres, or similar

-- Experience with ETL and job scheduling or orchestration using tools like Apache Airflow

-- Programming experience and familiarity with Python, AWS, and Git

-- Excellent communication and ability to work on a growing team

Bonus points if you have

-- Experience with web-scale data or working with healthcare data in a HIPAA-compliant environment

-- Experience with data modeling and BI tools like Looker

-- Experience with the Snowflake data warehouse, or with similar technologies in AWS

-- Experience with ETL orchestration frameworks like Apache Airflow

-- Experience with AB Testing

-- Experience with machine learning

Benefits

-- Stock Options

-- Unlimited PTO

-- Medical, Dental, Vision

-- 401k

-- Dogs in the office!