Senior Staff Data Engineer

Role Overview

• 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.
• Drive Engineering Excellence: 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.
• Improve Developer Efficiency: Implement solutions that measurably improve developer efficiency and establish engineering-wide quality and best practices.
• Deliver at Scale: Roll out major features and systems reliably, including appropriate monitoring, failure domain characterization, and success metric definitions.
• Align with Business Goals: Leverage a deep understanding of SmithRx’s business strategy to identify group-wide opportunities. Proactively refocus team efforts when projects are off-course or not moving the needle for the business.
• Manage Data Quality & Governance: Enforce data governance policies (PII/PHI protection, security, compliance) and implement data quality principles to raise the bar for the reliability of data shared internally and externally.
• Influence Cross-Functionally: Influence the roadmaps of other SmithRx teams. Act thoughtfully and decisively in critical situations, seeking diverse perspectives but ultimately leading decision-making to move priorities forward.
• Mentor and Develop: Serve as a role model and coach for other engineers, taking into account their unique skills and providing constructive feedback to maximize their impact.
• Executive Communication: Develop focused messaging and effectively present technical strategies and business cases at the executive level.
• Champion Organizational Change: Break down silos, build deep cross-functional relationships, and create excitement to drive the adoption of new technologies or processes across the organization.

Requirements

• 8+ years of 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.
• Demonstrated mastery of data modeling concepts, database design principles, and data warehouse technologies (e.g., Snowflake) through production-grade implementations.
• Strong skills in PySpark, SQL, and Python are required.
• Experience in modern object-oriented or compiled languages such as C#/C++, Go, Java, or Scala is a plus.
• Hands-on experience with leading ETL tools and frameworks (e.g., Apache Spark, Apache Airflow, dbt, Looker, Superset).
• In-depth experience managing the entire data lifecycle, with direct responsibility for the development, implementation, and production release of complex data processing solutions utilizing distributed systems.
• A proven track record of making decisions optimized for the wider engineering organization rather than locally optimal outcomes, especially in environments with significant ambiguity.

Tech Stack

• Airflow
• Apache
• Distributed Systems
• ETL
• Java
• PySpark
• Python
• Scala
• Spark
• SQL
• Go

Benefits

• Highly competitive wellness benefits including Medical, Pharmacy, Dental, Vision, and Life Insurance and AD&D Insurance
• Flexible Spending Benefits
• 401(k) Retirement Savings Program
• Short-term and long-term disability
• Discretionary Paid Time Off
• 12 Paid Company Holidays
• Wellness Benefits
• Commuter Benefits
• Paid Parental Leave benefits
• Employee Assistance Program (EAP)
• Well-stocked kitchen in office locations
• Professional development and training opportunities