Associate Data Engineer
About this opportunity
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.
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Job details
How this role compares
Computed from every other active Data & Digital role in our database, not just this employer's listings.
We currently track 226 comparable Associate Data & Digital roles across 58 biopharma companies.
Salary context
65 of 226 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 226 comparable roles
+ 25 more countries
Seniority mix
226 of 226 peers have a known seniority level
Therapeutic area mix
12 of 226 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
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How we calculate "similar"
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Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.