Data Engineer

Who We Are:

SmithRx is a rapidly growing, venture-backed Health-Tech company.  Our mission is to disrupt the expensive and inefficient Pharmacy Benefit Management (PBM) sector by building a next-generation drug acquisition platform driven by cutting edge technology, innovative cost saving tools, and best-in-class customer service.  With hundreds of thousands of members onboarded since 2016, SmithRx has a solution that is resonating with clients all across the country.

We pride ourselves for our mission-driven and collaborative culture that inspires our employees to do their best work. We believe that the U.S healthcare system is in need of transformation, and we come to work each day dedicated to making that change a reality. At our core, we are guided by our company values:

• Integrity: Our purpose guides our actions and gives us confidence in the path ahead. With unwavering honesty and dependability, we embrace the pressure of challenging the old and exemplify ethical leadership to create the new.
• Courage: We face continuous challenges with grit and resilience. We embrace the discomfort of the unknown by balancing autonomy with empathy, and ownership with vulnerability. We boldly challenge the status quo to keep moving forward—always.
• Together: The success of SmithRx reflects the strength of our partnerships and the commitment of our team. Our shared values bind us together and make us one. When one falls, we all fall; when one rises, we all rise.

Job Summary:

On the Data Engineering team, we transform streams of data into insights that solve real-world Pharmacy Benefits Management (PBM) challenges. We empower our Data Analytics and Machine Learning teams to make drug pricing transparent and easy to understand. We deliver business impact by designing, implementing, and maintaining distributed systems to collect, aggregate, and govern pharmaceutical data. We leverage open-source tools like Airflow and DBT built on top of industry-leading data warehouses (e.g., Snowflake), utilize LLMs to optimize workflows, and use Python heavily in our daily work.

What you will do:

Architecture & Technical Strategy

• Define Technical Vision: Define the long-term data engineering strategy guided by company-wide priorities and engineering best practices.
• Design Ecosystems: Create coherent designs across multiple pipelines and API boundaries. Reduce complex concepts to foundational components and simplify infrastructure to lower maintenance costs.
• Make high-impact technical choices—including "build vs. buy" and framework selections— based on sound reasoning. Review designs to preemptively identify and resolve technical risks.