About this opportunity
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.
ABOUT THE ROLE
Role Description:
We seek a skilled Data Engineer to build and optimize our data infrastructure. As a key contributor, you will collaborate closely with cross-functional teams to design and implement robust data pipelines that efficiently extract, transform, and load data into our AWS-based data lake and data warehouse. Your expertise will be instrumental in empowering data-driven decision making through advanced analytics and predictive modeling.
Roles & Responsibilities:
Building and optimizing data pipelines, data warehouses, and data lakes on the AWS and Databricks platforms.
Managing and maintaining the AWS and Databricks environments.
Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring.
Maintain system uptime and optimal performance
Working closely with cross-functional teams to understand business requirements and translate them into technical solutions.
Exploring and implementing new tools and technologies to enhance ETL platform performance.
Functional Skills:
Must-Have Skills:
Proficient in SQL for extracting, transforming, and analyzing complex datasets from both relational and columnar data stores. Proven ability to optimize query performance on big data platforms.
Proficient in leveraging Python, PySpark, and Airflow to build scalable and efficient data ingestion, transformation, and loading processes.
Ability to learn new technologies quickly. Strong problem-solving and analytical skills. Excellent communication and teamwork skills.
Good-to-Have Skills:
Experienced with SQL/NOSQL database, vector database for large language models
Experienced with data modeling and performance tuning for both OLAP and OLTP databases
Experienced with Apache Spark, Apache Airflow
Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops
Experienced with AWS, GCP or Azure cloud services
Education and Professional Certifications
5 to 9 years of experience and Bachelor’s degree in computer science and engineering preferred, other Engineering field is considered
AWS Certified Data Engineer preferred
Databricks Certificate preferred
Soft Skills:
Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills
Ability to work effectively with global, virtual teams
High degree of initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented, with a focus on achieving team goals
Strong presentation and public speaking skills
.
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 916 comparable Data & Digital roles across 90 biopharma companies.
Salary context
257 of 916 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 916 comparable roles
+ 38 more countries
Seniority mix
580 of 916 peers have a known seniority level
Therapeutic area mix
33 of 916 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
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.