INTL - Data Analytics Engineer

Position Overview: As an Analytics Engineer at GoodRx, you will be at the forefront of

transforming raw data into reliable enterprise data assets that enable strategic decision-making

across the organization. Your primary focus will be building and productionalizing new

Subscriptions data sets from the ground up, ensuring they support universal analytics needs for

critical aspects of the business. You will collaborate closely with stakeholders to understand

business needs, translate domain expertise into actionable data logic, and help shape the future

of data-driven insights at scale.

This role requires deep technical expertise in analytics engineering, a keen understanding of

stakeholder needs, and the ability to drive alignment on data sets that serve as the single

source of truth. You will support the creation and initial operationalization of data pipelines, while

ensuring smooth transitions to long-term ownership by data engineering teams.

Build Enterprise Data Assets (0 ? 1): Lead efforts to create new data sets from

scratch, focusing on foundational analytics assets that serve universal business

purposes.

? Enable Robust & Extensible Analytics: Establish widely accepted logic for critical data

sets, driving alignment on a single source of truth that reduces confusion, rework, and

analytical overhead.

? Set Analytics Requirements: Collaborate with business stakeholders to capture data

requirements, ensuring that new data assets are both fit for purpose and future-proof.

? Translate Domain Expertise into Data Logic: Work with domain experts to convert

their knowledge into computational logic that underpins new data sets.

? Productionalize Data Sets: Build and deploy v1 data sets in a way that allows the

business to benefit immediately from their insights, while ensuring scalability and

maintainability.

? Alerting & Monitoring: Implement alerting mechanisms to ensure that data sets are

monitored effectively, with issues flagged to appropriate teams for timely resolution.

? External Reporting Support: Enable data exports to external parties by supporting

development and testing of reporting data sets.

? Change Management Support: Help stakeholders manage changes to business logic

and analytics requirements, especially when upstream data sets evolve (e.g., claim

4+ years of experience in data engineering, analytics engineering, or related fields, with

a proven track record of building and maintaining large-scale data assets.

? Expertise in SQL for querying and data transformation.

? Strong programming skills in Python for data manipulation, automation, and building

data pipelines. Experience with frameworks like Pandas, NumPy, and PySpark is

preferred.

? Experience with cloud data platforms such as Snowflake, BigQuery, or AWS

Redshift, including working with cloud-native tools for data integration and

transformation.

? Experience with ETL orchestration tools such as Airflow for managing and scheduling

DAGs, ensuring that workflows are efficient, reliable, and scalable.

? Familiarity with data modeling concepts such as star/snowflake schemas and building

logical and physical data models for analytics use cases.

? Experience working with version control systems like Git for collaboration and

maintaining code integrity.

? Proficiency with BI tools such as Tableau, Looker, or Power BI for dashboarding and

data visualization.

? Experience with alerting and monitoring tools like Datadog, PagerDuty, or Grafana

for ensuring data pipeline health and resolving issues proactively.

? Familiarity with CI/CD pipelines and experience with DevOps practices in a data

engineering context.

? Experience working with data governance and data quality frameworks to ensure

compliance and accuracy of enterprise data.

? Strong understanding of analytics workflows, from data collection to processing and

analysis, with experience in data lineage and data cataloging tools (e.g., Alation,

Collibra).

? Excellent problem-solving and communication skills, with the ability to navigate complex

business needs and translate them into technical requirements. Experience in the healthcare, health-tech, or similar industries, with exposure to working

with healthcare data (e.g., claims, EHR, or HCP data).

? Familiarity with agile development methodologies and working in cross-functional

teams.

? Prior experience working with diverse data types, including event data, transactional

data, and marketing data.