About this role
Data engineers on the team build distributed systems and design pipelines to collect, aggregate, and govern pharmaceutical data that supports data analytics and machine learning efforts. Designing coherent ecosystems across multiple pipelines, defining technical strategy, and enforcing data governance policies like PII and PHI protection occupy the daily routine. SmithRx created this position to scale its drug acquisition platform and transform the pharmacy benefit management sector by turning complex data streams into transparent insights. Engineers who thrive here balance autonomy with empathy, embrace ambiguity, and make high-impact technical choices based on sound reasoning rather than local optimization.
What you will do
- Define the long-term data engineering strategy and create coherent designs across multiple pipelines and API boundaries.
- Roll out major distributed features and systems reliably with appropriate monitoring and failure domain characterizations.
- Enforce data governance policies including PII and PHI protection, security, and compliance.
- Mentor other engineers, provide constructive feedback, and serve as a technical role model.
- Present technical strategies and business cases effectively at the executive level.
- Collaborate cross-functionally to influence product roadmaps and drive the adoption of new technologies.
What you need
- Possess 8+ years of industrial experience in data engineering with an advanced degree, or 12+ years with an undergraduate degree in Computer Science, Information Technology, or a related field.
- Demonstrate mastery of data modeling concepts, database design principles, and data warehouse technologies like Snowflake through production-grade implementations.
- Bring strong coding skills in PySpark, SQL, and Python.
- Manage the entire data lifecycle with direct responsibility for developing, implementing, and releasing complex data processing solutions using distributed systems.
- Show a proven track record of making architectural and framework decisions optimized for the wider engineering organization in ambiguous environments.
Nice to have
- Start-up and healthcare experience
- Experience in modern object-oriented or compiled languages such as C#/C++, Go, Java, or Scala
- Hands-on experience with leading ETL tools and frameworks like Apache Spark, Apache Airflow, dbt, Looker, and Superset
Benefits
- Medical, Pharmacy, Dental, Vision, and Life and AD&D Insurance
- Flexible Spending Benefits and 401(k) Retirement Savings Program
- Short-term and long-term disability
- Discretionary Paid Time Off and 12 Paid Company Holidays
- Paid Parental Leave benefits and Employee Assistance Program
- Professional development and training opportunities
Skills and tech stack
Questions candidates ask
Is this a remote position?
Yes, the role is remote, though compensation varies depending on your geographic work location zone across the United States.
What is the salary range for this role?
Base salary ranges depend on geographic zones, spanning from $178,500 to $210,000 USD in Zone C, $196,400 to $231,000 USD in Zone B, and $214,200 to $252,000 USD in Zone A.
What tools and technologies will I use daily?
Daily work heavily utilizes Python, SQL, and PySpark, along with open-source tools like Airflow and dbt built on top of data warehouses such as Snowflake and leveraging LLMs to optimize workflows.
What kind of experience is required?
Candidates need 8+ years of industrial data engineering experience with an advanced degree, or 12+ years with an undergraduate degree in Computer Science, Information Technology, or a related field.