This is where your work makes a difference.
At Baxter, we believe every person—regardless of who they are or where they are from—deserves a chance to live a healthy life. It was our founding belief in 1931 and continues to be our guiding principle. We are redefining healthcare delivery to make a greater impact today, tomorrow, and beyond.
Our Baxter colleagues are united by our Mission to Save and Sustain Lives. Together, our community is driven by a culture of courage, trust, and collaboration. Every individual is empowered to take ownership and make a meaningful impact. We strive for efficient and effective operations, and we hold each other accountable for delivering exceptional results.
Here, you will find more than just a job—you will find purpose and pride.
We are seeking a highly motivated and experienced Senior Data Engineer to design, develop, and implement enterprise-scale data integration and data engineering solutions. The ideal candidate will have strong expertise in PySpark, Python, SQL, AWS, Snowflake, and API integrations , with experience delivering end-to-end data engineering projects from requirements gathering through production deployment.
The role involves building scalable data pipelines, modernizing data platforms, integrating enterprise applications, and supporting business analytics through reliable and high-performing data solutions.
Key Responsibilities
Design, develop, and maintain end-to-end data integration and engineering solutions.
Lead the complete project lifecycle including requirements analysis, solution design, development, testing, deployment, and production support.
Build and optimize scalable ETL/ELT pipelines for data ingestion, transformation, and loading.
Develop robust data processing frameworks using PySpark and Python.
Design and optimize complex SQL queries and data transformations.
Develop and support integrations using REST APIs and modern integration patterns.
Implement and maintain cloud-based data solutions leveraging AWS services and Snowflake.
Ensure data quality, performance, scalability, and reliability across data platforms.
Collaborate with business stakeholders, architects, and application teams to deliver effective data solutions.
Troubleshoot production issues and provide timely resolutions.
Follow data governance, security, and compliance standards.
Mentor junior team members and contribute to data engineering best practices.
Primary Skills
PySpark
Strong hands-on experience in PySpark development for large-scale distributed data processing.
Experience developing and optimizing Spark applications for high-volume data workloads.
Knowledge of Spark DataFrames, Spark SQL, performance tuning, and cluster optimization.
Python
Strong programming skills in Python.
Experience building reusable frameworks, data pipelines, and automation solutions.
Knowledge of object-oriented programming and data processing libraries.
SQL
Advanced SQL development and performance tuning.
Strong experience in writing complex queries, stored procedures, and data transformations.
Good understanding of data modeling and data warehousing principles.
AWS
Hands-on experience with AWS services including: Amazon S3 AWS Glue AWS Lambda Amazon EMR AWS Step Functions Amazon RDS Amazon CloudWatch
Strong experience with Snowflake Data Cloud.
Knowledge of cloud-based data architecture and data lake implementations.
Data Engineering & Integration
Strong understanding of ETL/ELT concepts and data integration patterns.
Experience in end-to-end implementation of data engineering solutions.
Strong knowledge of data warehousing, data modeling, and data migration concepts.
Tools & Methodologies
Git and version control systems.
CI/CD and DevOps practices.
Agile and Scrum methodologies.
Secondary skills
Informatica PowerCenter / IICS
Experience developing and supporting ETL workflows using Informatica PowerCenter and/or Informatica Intelligent Cloud Services (IICS).
Knowledge of data integration, transformation,…