Associate Data Engineer
Amgen
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9,462 open data roles across 953 companies are on ApplySarthi right now, most of them in Bengaluru (510), Hyderabad (337), Mumbai (160).
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What data roles keep asking for: Python (38%), SQL (33%), AWS (30%), Machine learning (19%), Spark (15%), Databricks (14%), Stakeholder management (13%), Azure (13%) — counted across their open postings here.
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Amgen has 1,937 open roles listed here.
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Counted across 14 company job boards, updated as roles open and close.
Preparing for this interview
Interviews for data roles keep coming back to Python, SQL, AWS, Machine learning. Practise those questions before you sit with Amgen.
Questions you are likely to be asked
- Why do you want to join Amgen?
- What is your experience with ETL? Tell me one thing you learned the hard way.
- How did you know your model was actually good, and not just good on your test set?
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
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Practise the Associate Data Engineer at Amgen interview free →Career Category Engineering Job Description Associate Data Engineer 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 3-5 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 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. Ready to Apply for the Job? We highly recommend utilizing Workday's robust Career Profile feature to complete the application process. A link to update your profile is available when you click Apply . You can then complete your Workday profile in minutes with the “Upload My Experience” functionality to upload an updated copy of your resume or you can simply edit the individual sections of your Career Profile. .
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Listed on workday · posted 2026-10-26. ApplySarthi collects openings and links to application pages; the role is advertised by Amgen, not by us.