Associate Data Engineer

Amgen; India; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Amgen seeks an experienced data engineer to design and maintain scalable data pipelines using Python, PySpark, and Databricks in an agile environment. You will ingest and transform diverse data sources, ensure data quality, and support analytics, reporting, and ML workflows.

You will collaborate with data engineers, analysts, and scientists to deliver reliable datasets, optimize performance, and document data processes. Strong SQL, cloud familiarity, and version control are essential.

Qualifications

• Bachelor's degree in CS or related field required.
• Hands-on Python for data processing and automation.
• Strong PySpark and distributed processing knowledge.
• Databricks experience with notebooks, clusters, jobs and Delta tables.
• Ability to build, maintain, and troubleshoot scalable ETL/ELT pipelines in Databricks.
• SQL for data querying, transforming and validating data.
• Basic understanding of AI/ML concepts and model inputs/outputs.
• Experience with cloud platforms (AWS/Azure/GCP).
• Familiarity with Git and version control.
• Strong analytical and collaborative skills.

Responsibilities

• Develop, test, and maintain data pipelines using Databricks, PySpark, and Python.
• Ingest, transform, and process structured and semi-structured data from multiple sources.
• Support scalable ETL/ELT workflows for analytics, reporting, and ML use cases.
• Collaborate with data engineers, analysts, and data scientists to deliver reliable datasets.
• Perform data cleansing, validation, and quality checks for accuracy.
• Optimize Spark jobs and Databricks notebooks for performance and cost.
• Create and maintain documentation for pipelines and data definitions.
• Troubleshoot pipeline failures, data issues, and bottlenecks.
• Follow best practices for version control, testing, and deployment.
• Support basic AI/ML data prep, feature engineering, and model inputs.
• Monitor scheduled jobs and workflows for timely delivery.
• Work in an Agile or iterative development environment.

Skills

Python

PySpark

Databricks

SQL

Delta Lake

ETL pipelines

Data quality

Data modeling

Git

Cloud platforms

Analytical thinking

Education

Bachelor's in CS or related field

Equivalent practical experience

Tools

Databricks

Delta Lake

MLflow

Tableau

Power BI

Airflow

Job description

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.

Roles & Responsibilities

• Develop, test, and maintain data pipelines using Databricks, PySpark, and Python.
• Ingest, transform, and process structured and semi-structured data from multiple sources.
• Support the development of scalable ETL/ELT workflows for analytics, reporting, and machine learning use cases.
• Work with data engineers, analysts, and data scientists to understand data requirements and deliver reliable datasets.
• Perform data cleansing, validation, and quality checks to ensure accuracy and consistency.
• Optimize Spark jobs and Databricks notebooks for performance, reliability, and cost efficiency.
• Create and maintain documentation for data pipelines, workflows, data definitions, and processes.
• Assist in troubleshooting pipeline failures, data issues, and performance bottlenecks.
• Follow best practices for version control, code quality, testing, and deployment.
• Support basic AI/ML data preparation activities, including feature engineering, dataset creation, and model input preparation.
• Monitor scheduled jobs and workflows to ensure timely and successful data delivery.
• Collaborate with cross-functional teams in an Agile or iterative development environment.

Basic Qualifications and Experience

2-6 years of experience with Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience

Must-Have Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field, or equivalent practical experience.
• Hands-on experience with Python for data processing, scripting, and automation.
• Strong working knowledge of PySpark and distributed data processing concepts.
• Proven hands-on experience using Databricks for data engineering, including notebooks, clusters, jobs, workflows, Delta tables, and performance optimization.
• Ability to build, maintain, and troubleshoot scalable ETL/ELT pipelines in Databricks.
• Experience working with Delta Lake and lakehouse architecture concepts.
• Working knowledge of SQL for querying, transforming, and validating data.
• Ability to work with structured and semi-structured data formats such as CSV, JSON, Parquet, and Delta.
• Understanding of data engineering concepts such as ETL/ELT, data pipelines, data lakes, data warehouses, batch processing, and data quality.
• Basic understanding of AI and machine learning concepts, including features, training datasets, model inputs/outputs, and model evaluation basics.
• Experience supporting data preparation or feature engineering for AI/ML use cases.
• Familiarity with cloud-based data platforms, preferably AWS, Azure, or GCP.
• Understanding of Git or other version control tools.
• Strong analytical, problem-solving, and troubleshooting skills.
• Good communication skills and ability to work collaboratively with technical and non-technical stakeholders.
• Willingness to learn new tools, technologies, and data engineering best practices.

Preferred Qualifications

• Exposure to Delta Lake, Unity Catalog, or Lakehouse architecture.
• Experience with workflow orchestration tools or Databricks Jobs.
• Familiarity with CI/CD practices for data engineering projects.
• Exposure to machine learning workflows using MLflow, scikit-learn, or similar tools.
• Experience with Tableau, Power BI, or similar data visualization tools to create dashboards, support reporting needs, validate datasets, and perform exploratory analysis.
• Understanding of data governance, security, and access control concepts.
• Experience working in an Agile/Scrum environment.