Walgreens: Data Engineer

Job Post; Town of Texas (WI); On-site; USD 100,000 - 150,000; Full time

Job summary

Walgreens is seeking a Data Engineer to design, develop, and maintain scalable data pipelines using Python, SQL, Spark/PySpark, and cloud platforms. You will leverage Databricks, Snowflake, Airflow, and dbt to build robust data solutions that support analytics and decision-making, while collaborating with cross-functional teams to modernize data infrastructure.

The role requires leadership experience at the senior level and involves working with complex data architectures in a fast-paced

Qualifications

• Senior Leadership experience overseeing multiple departments or directors.

Responsibilities

• Design, develop, and maintain scalable data pipelines and data solutions.
• Work with Python, SQL, Spark/PySpark to implement ETL/ELT processes.
• Utilize Databricks, Snowflake, Airflow, and dbt for data orchestration and modeling.
• Collaborate with cross-functional teams to translate requirements into reliable data solutions.
• Troubleshoot production issues and optimize performance and scalability.

Skills

Python

SQL

Spark/PySpark

cloud technologies

Tools

Databricks

Snowflake

Airflow

dbt

Job description

Walgreens: Data Engineer

LOCATION

TX

SALARY

$100000 - $150000 (Yearly)

COMPANY

Walgreens

DEPARTMENT

Data

EMPLOYMENT TYPE

Other

MINIMUM LEVEL OF EXPERIENCE

Senior Leadership (overseeing multiple Department Directors. Ex: Campaign Manager, Coordinated Manager)

APPLY BY

October 1, 2026

JOB DESCRIPTION

Design, develop, and maintain scalable data pipelines and data solutions using Python, SQL, Spark/PySpark, Databricks, Snowflake, Airflow, dbt, and cloud technologies. Develop batch and near-real-time data ingestion and transformation processes, implement data quality and validation checks, and optimize data processing for performance and scalability. Work with cross-functional teams to translate business requirements into reliable data solutions that support analytics, reporting, and decision-making. Troubleshoot production issues, monitor pipeline performance, document data workflows, and collaborate with other developers and platform teams to support ongoing maintenance. Contribute to modernization of legacy ETL processes and migration to cloud-based data