CAREER CATEGORY

Engineering

JOB DESCRIPTION

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest
diseases, and make people’s lives easier, fuller and longer. We discover,
develop, manufacture and deliver innovative medicines to help millions of
patients. Amgen helped establish the biotechnology industry more than 40 years
ago and remains on the cutting-edge of innovation, using technology and human
genetic data to push beyond what’s known today.

Role Description

Let’s do this. Let’s change the world.

We are seeking an experienced Data Engineer to design, develop, and support
scalable data pipelines and integration solutions. This role will work with
large and complex datasets to ensure data is reliable, accessible, secure, and
available for analytics, reporting, and AI use cases.

The ideal candidate has solid hands-on experience with Databricks, Apache
Spark, Python, SQL, cloud technologies, data modeling, and ETL/ELT processes.
The candidate will collaborate with data architects, business teams, data
scientists, product teams,



and DevOps teams to deliver high-quality data
solutions.

Experience in biotechnology, pharmaceutical, manufacturing, life sciences, or
another regulated industry is preferred.

Roles and Responsibilities

* Design, develop, test, and maintain scalable data pipelines and
data-integration solutions.

* Build ETL/ELT processes for structured, semi-structured, and unstructured
data.

* Integrate data from enterprise applications, databases, APIs, cloud
platforms, and third-party systems.

* Contribute to the technical design and implementation of end-to-end data
solutions.

* Develop reusable and maintainable Python, PySpark, Spark SQL, and SQL
components.

* Implement data-quality checks, validation rules, reconciliation processes,
logging, and exception handling.

* Optimize Spark workloads, SQL queries, partitioning, and data-processing
performance.

* Develop and maintain data models, data dic

📌 Data Engineer (Hyderabad)
🏢 AMGEN
📍 Hyderabad