Principal Data Engineer

Responsibilities

• Define the long-term technical vision and architecture for the Data Platform, partnering with Product, Engineering, Data Science, and Analytics leaders to align platform investments with business strategy.
• Provide technical leadership and mentorship across multiple engineering teams, influencing architectural decisions, raising engineering standards, and developing future technical leaders.
• Lead the architectural evolution of the data platform to improve scalability, reliability, operational efficiency, and long-term maintainability.
• Drive investments in developer experience, platform tooling, CI/CD automation, observability, and self-service capabilities that improve engineering productivity and platform reliability.
• Develop architectural proposals, technical standards, and engineering design documentation that guide long-term platform strategy and evolution.
• Lead complex platform modernization initiatives, large-scale migrations, and architectural transformations with minimal disruption to the business.
• Establish organization-wide standards for data architecture, engineering quality, platform governance, and reusable engineering patterns.
• Drive long-term engineering and platform strategy through data-informed decision-making, balancing scalability, reliability, cost efficiency, and developer productivity.
• Lead enterprise-scale platform initiatives that enable analytics, machine learning, AI workloads, and scalable product development across GoodRx.

Qualifications

• 10+ years of experience in data engineering, including demonstrated experience defining technical strategy and influencing engineering organizations beyond a single team.
• Experience with one of the cloud environments - AWS, GCP.
• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
• Experience designing, building, and evolving enterprise-scale data platforms that support multiple engineering teams, analytics, and machine learning workloads.
• Innately curious and organized with the drive to analyze data to identify deliverables, anomalies and gaps and propose solutions to address these findings.
• Experience designing data models that have been implemented in production.
• Strong experience in writing complex SQL and ETL development with experience processing large data sets.
• Familiarity with AWS Services (Redshift, RDS, EKS, S3, EMR, Glue, Lambda).
• Experience evaluating and applying AI-assisted engineering tools and modern data platform technologies to improve engineering productivity and platform reliability.
• Experience with modern software development, infrastructure-as-code, containerization, and CI/CD tooling (e.g., GitHub, Docker, Terraform, CodeFresh, Jira).
• Experience contributing to full lifecycle deployments with a focus on quality and scalability.

Skills

• Experience with session data such as Google Analytics & Segment.
• Experience related to data privacy like CCPA & GDPR (Data deletion, data de-identification, Right to forget and such).
• Experience with data quality processes, data quality checks, validations, data quality metrics definition and measurement.
• Experience with lakehouse architectures (Delta Lake, Apache Iceberg, or similar), metadata management, or enterprise data governance platforms.
• AWS/Databricks/GCP or similar certifications.

Pay

Pay ranges are determined based on work locations and may vary based on where the successful candidate is hired. The pay ranges below are shown as a guideline, and the successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, and other relevant business and organizational factors. These pay zones may be modified in the future. Please contact your recruiter for additional information.