Senior Data Engineer

Overview

Join the team behind iconic brands like Huggies®, Kleenex®, Cottonelle®, Scott®, Kotex®, Poise®, Depend®, and Kimberly‑Clark Professional®. At Kimberly‑Clark, you’ll find innovation, growth, and the opportunity to make a real impact. You were made to create Better Care for a Better World—by designing new technologies, working with data, optimizing digital experiences, and delivering better, faster results. Be part of a performance‑driven culture where technology has purpose, and where sustainability, inclusion, wellbeing, and career growth are integral. It starts with YOU.

About You

In one of our technical roles, you’ll focus on winning with consumers and the market, while putting safety, mutual respect, and human dignity at the center. A Sr. Data Engineer is responsible for transforming raw data into comprehensible formats that can be effectively analyzed and reported. This position involves analysis of data to be transported, conceptual design for loading data across various platforms, and key technical integrations to ensure success. To succeed in this role, you will need the following qualifications:

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 6–10 years of hands‑on experience in data engineering, large‑scale distributed data systems, or analytics infrastructure.
• Strong experience with: PySpark, SQL, and distributed data processing frameworks and Azure (preferred), AWS or GCP cloud data stack (Data Lake, Data Factory, Synapse, Delta Lake, Databricks)
• Data pipeline orchestration (Airflow, ADF, Prefect, etc.) & CI/CD, Git, DevOps/MLOps workflows.
• Demonstrated experience partnering with data scientists and ML engineers to deliver production‑grade data workflows.
  
  

• Develop, deploy, and maintain data pipelines and data warehousing solutions, including ETL processes.
• Perform data modeling and management to ensure high performance and scalability.
• Collaborate with cross‑functional teams using Agile methodology to define and track data processing metrics.
• Use SQL and programming languages to build, manage, and optimize data infrastructure.
• Ensure data quality through effective problem‑solving, troubleshooting, and technical communication.
• Continuously learn and stay current with IT advancements, especially AWS and Microsoft Azure.
• Strong hands‑on experience with SQL and Python.
• Experience working with Snowflake data lake.
• Experience building data pipelines using Azure Data Factory.
  
  

Click the Apply button and complete the online application process. A member of our recruiting team will review your application and follow up if you seem like a great fit for this role.

In the meantime, please check out the careers website.

And finally, the fine print….

For Kimberly-Clark to grow and prosper, we must be an inclusive organization that applies the diverse experiences and passions of its team members to brands that make life better for people all around the world. We actively seek to build a workforce that reflects the experiences of our consumers. When you bring your original thinking to Kimberly-Clark, you fuel the continued success of our enterprise. We are a committed equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation, gender identity, age, pregnancy, genetic information, citizenship status, or any other characteristic protected by law.

The statements above are intended to describe the general nature and level of work performed by employees assigned to this classification. Statements are not intended to be construed as an exhaustive list of all duties, responsibilities and skills required for this position.

Employment is subject to verification of pre-screening tests, which may include drug screening, background check, and DMV check.

Primary Location

IT Centre Bengaluru GDTC

Additional Locations

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time