Blink Health is fixing how broken, opaque, and unfair healthcare is. We are a New York based, mission driven, well-funded healthcare technology company. We’re changing healthcare through technology and transparency. With our proprietary technology, everyone now has access to one, low negotiated price on over 15,000 medications. But there is more work to do.
About The Team
Blink Engineering strives to build trusted, highly observable, data-driven products to bring affordable, accessible healthcare to all Americans. We understand healthcare is the most complex system most of us will ever fix. We believe in solving this complexity through the use of simple, well-known technologies. We are a highly collaborative team that believes in owning outcomes over owning code and putting patients at the center of everything we do.
The Blink Health Data Engineering and Analytics team is a small team responsible for building infrastructure, frameworks and tooling to enable data-driven decisions; building and maintaining our data warehouse for security and scale. This role is central to building and executing on a robust and forward-looking data strategy for the company, and the successful candidate blends top-tier software engineering expertise with the ability to look ahead at what we need to build for the future.
About the Role
As the senior software engineer for data, you will be a thought leader within the data engineering team that is designing and building our next generation of data tools and frameworks, in addition to developing and maintaining data products and infrastructure. You will proactively assess production DW support trends to determine and implement short- and long-term solutions, and be able to design for data integrity, reliability, and performance. You will set a high bar for clean and correct code, setting code standards, and performing peer code and architecture reviews.
- You have 6+ years hands-on experience and demonstrated strength with:
- Python software development.
- Building and maintaining robust and scalable data integration (ETL) pipelines using SQL, EMR, Python and Spark.
- Writing complex, highly-optimized SQL queries across large data sets.
- Designing and maintaining columnar databases (e.g., Redshift, Snowflake)
- Distributed data processing (Hadoop, Spark, Hive)
- ETL with batch (AWS Data Pipeline, Airflow) and streaming (Kinesis)
- Integration and design for Business Intelligence tools (e.g., Looker, QuickSight)
- Creating scalable data models for analytics.
- You have experience designing and refactoring large enterprise data warehouses and associated ETLs, with continuous improvement examples for automation and simplification across all aspects of the DW environment, inclusive of both engineering and business reporting.
- Experience owning features from design through delivery along with ongoing support.
- Proven success with communicating effectively across diverse disciplines (including product engineering, infrastructure, analytics, data science, finance, marketing, customer support, etc.) to collect requirements and describe data engineering strategy and decisions.
- Experience providing clear data engineering technical leadership, mentoring, and best practices for data management and quality within and across teams.
- Undergraduate or graduate degree in Computer Science
- Healthcare-relevant company experience as part of the required experience above, with demonstrated industry knowledge of handling sensitive information.