Data Engineer (AWS/Databricks) - Amgen (Build Pipelines that Save Lives)

Most Data Engineers spend their days optimizing ad clicks. They write code to make sure you see the same pair of sneakers on every website you visit until you finally buy them. It pays the bills, but does it feel meaningful? Probably not.

Now, imagine writing code that helps cure cancer.

I am not being dramatic. At Amgen, data isn't just numbers in a spreadsheet; it’s patient lives. It’s clinical trial results. It’s the genetic code of a virus. When you build a pipeline here, you aren't just moving data from Point A to Point B; you are accelerating the speed at which a life-saving drug reaches a patient. If you are tired of working on "boring" fintech or e-commerce data and want to use your Python and SQL skills for something that actually matters, this is the role. This is where your code meets biology.

1. Why This Job is an Amazing Opportunity

✅ You Will Master the "Modern Data Stack" (Databricks + AWS)

A lot of companies are still stuck on legacy on-premise servers. Amgen is not one of them. The job description explicitly mentions Databricks and AWS. This is the Ferrari of data engineering. By joining this team, you will learn the "Lakehouse" architecture—the cutting-edge blend of Data Lakes and Data Warehouses. You will master PySpark for big data processing and Airflow for orchestration. These are the most in-demand skills in the market right now. You are future-proofing your career for the next decade.

✅ A Rare Chance to Work with "Vector Databases" & LLMs

Did you catch that line in the "Good-to-Have" skills? "Experienced with vector database for large language models." This is huge. Most Data Engineering jobs are still just doing basic ETL (Extract, Transform, Load). Amgen is moving into GenAI. They want you to help build the infrastructure for Large Language Models (LLMs) that can read medical research or analyze drug data. Gaining experience with Vector DBs (like Pinecone or Milvus) puts you in the top 1% of Data Engineers globally.

✅ Recession-Proof Stability

Tech startups are volatile. One bad quarter and layoffs happen. The pharmaceutical industry is different. People need medicine regardless of the economy. Amgen is a biotechnology pioneer with a 40-year history. It offers the stability of a massive enterprise combined with the innovation budget of a tech giant. You get a safe paycheck, great benefits, and the peace of mind that comes with working for an industry leader.

2. Role Details

3. The "What, How, & Why" of This Role

What You Will Actually Do:

You are the architect of the data highway.

Your day won't be spent manually fixing Excel sheets. You will be building automated pipelines. Imagine Amgen runs a clinical trial with 5,000 patients. That data comes in messy—different formats, missing values. Your job is to write a PySpark job on Databricks that ingests this raw data from AWS S3, cleans it, transforms it into a usable format (like Delta tables), and loads it into a Data Warehouse so scientists can analyze it. You will also monitor these pipelines using Airflow to ensure they don't crash at 3 AM.

How You Can Succeed in the First 90 Days:

• Month 1 (The Explorer): Focus on the domain. What is a "Clinical Trial"? What is "Genomic Data"? Understand the data you are handling. Get your access to the AWS Console and Databricks workspace sorted.

• Month 2 (The Builder): Pick a small, broken pipeline. Maybe a specific daily report is failing because the data volume spiked. optimize the SQL query. Tune the Spark configuration. Show them you understand performance.

• Month 3 (The Innovator): The JD asks for "New tools." Propose a way to use a Vector Database to search through unstructured PDF reports. This aligns perfectly with their "Good-to-Have" wish list.

Why This Role is a Stepping Stone:

Data Engineers who know Biotech Data are unicorns. If you work here for 2 years, you can easily pivot to become a Bioinformatics Engineer, a Machine Learning Engineer (since you know Vector DBs), or a Data Architect. The salary jump for these specialized roles is massive compared to a generic Data Engineer.

4. Interview Preparation Guide (With Master Class Resources)

We have researched the specific technical topics candidates must revise for an Amgen Data Engineering interview.

Where to Practice:

• SQL: Go to LeetCode or StrataScratch. Focus on "Hard" level questions involving Window Functions (RANK, LEAD, LAG). They need you to analyze "complex datasets," not just do simple joins.

• Python: Practice data manipulation on HackerRank. Focus on dictionaries, list comprehensions, and pandas.

5. Key Concepts to Revise (Deep Syllabus)

Concept 1: Apache Spark & PySpark Optimization

Focus: Transformations vs. Actions, Lazy Evaluation, and handling Data Skew

Master Video: PySpark Optimization Techniques

You cannot just write code that runs; you must write code that scales. This concept focuses on the internal mechanics of Spark—specifically how "Lazy Evaluation" builds a plan before execution and how to resolve "Data Skew" when one partition overloads the memory.

Concept 2: AWS Data Services (S3, Glue, EMR)

Focus: S3 architecture, Glue (Serverless ETL) vs. EMR (Managed Hadoop), and selection criteria

Master Video: AWS Big Data Specialty - Glue vs EMR Explained

This tests your ability to choose the right tool for the job. You need to articulate the architectural differences between AWS Glue (best for serverless, intermittent jobs) and EMR (best for persistent, heavy-lifting Hadoop clusters).

Concept 3: Workflow Orchestration (Apache Airflow)

Focus: DAG (Directed Acyclic Graph) design, Task Scheduling, Operators, and Sensors

Master Video: Apache Airflow for Beginners - Marc Lamberti

Orchestration is the backbone of data engineering. You need to understand how to define dependencies between tasks using DAGs, and specifically how "Sensors" wait for external events (like a file landing in S3) to trigger a pipeline.

Concept 4: Data Modeling (Star vs. Snowflake)

Focus: Warehouse design principles, Star Schema structure, and Fact vs. Dimension tables

Master Video: Data Warehousing - Fact vs Dimension Tables - Kimball Method

The foundation of good reporting is a clean data model. This concept requires you to distinguish between a "Fact Table" (measurements/metrics) and a "Dimension Table" (context/attributes) and know when to denormalize data into a Star Schema for performance.

Concept 5: Delta Lake Architecture

Focus: ACID transactions on Data Lakes, Time Travel (Data Versioning), and Databricks integration

Master Video: What is Delta Lake? - Databricks Official Channel

Since Amgen uses Databricks, understanding Delta Lake is critical. It solves the "swamp" problem by adding reliability (ACID transactions) to S3, allowing you to update, delete, and even "Time Travel" back to previous versions of your data.

Concept 6: Vector Databases (The Bonus Round)

Focus: Vector Embeddings, Semantic Search, and text-to-number conversion

Master Video: Vector Databases Explained in 10 Minutes - IBM Technology

This is your competitive edge. It covers how modern AI applications store unstructured data (text/images) as "Vector Embeddings" to enable semantic search (finding data by meaning rather than just keywords).

Real-World Interview Questions:

❓ SQL: "Write a query to find the top 3 drugs sold per region for the last 6 months. (Hint: Use DENSE_RANK())."

❓ Spark: "I have a 100GB file and a 10MB file. I want to join them in PySpark. Which join strategy should I use?" (Answer: Broadcast Join).

❓ Architecture: "Design a data pipeline to ingest streaming data from IoT sensors in a manufacturing plant. What tools would you use on AWS?" (Answer: Kinesis -> Firehose -> S3 -> Databricks).

❓ Troubleshooting: "Your Airflow DAG failed at step 3. How do you debug it without re-running steps 1 and 2?"

❓ Scenario: "How do you handle schema evolution? What if the source system adds a new column today?"

❓ Behavioral: "Tell me about a time you had to explain a technical data issue to a non-technical stakeholder (like a scientist)."

6. Why Join Amgen?

A Culture of "Ethics First"

The job description opens with a strong statement about "integrity" and "honesty." Amgen takes this seriously. In an industry where cutting corners can hurt patients, they value character as much as coding skills. If you are someone who prides themselves on doing the right thing even when no one is looking, you will fit right in.

Innovation at the Edge

Amgen isn't resting on its laurels. They are actively "using technology and human genetic data to push beyond what’s known today." They are integrating AI, Machine Learning, and Big Data into the core of drug discovery. You will be working on projects that sound like science fiction—using data to predict how a protein will fold or how a patient will respond to treatment.

Global Collaboration

You will "work effectively with global, virtual teams." This means exposure. You will collaborate with colleagues in California, Europe, and Asia. This cross-cultural experience is invaluable and helps you grow not just as an engineer, but as a global professional.

7. FAQs

Q: Do I need a Biology degree?

A: No. The requirements list "Computer Science" or "Engineering." They need you for your coding skills. You will learn the domain knowledge on the job.

Q: Is "Databricks" mandatory?

A: The JD lists it as a "Must-Have" responsibility but implies they want proficiency in PySpark. If you know generic Apache Spark well, you can learn the Databricks platform nuances quickly.

Q: What is "GCF 4"?

A: GCF stands for "Global Career Framework." Level 4 typically corresponds to a Senior Associate or Mid-Level Engineer. It means you aren't a fresh grad, but you aren't a Staff Engineer yet. You are expected to work independently.

Q: Is this a remote role?

A: The JD mentions "virtual teams," which implies some flexibility, but Amgen usually operates on a Hybrid model. You will likely need to be in the office (Hyderabad/Bangalore) a few days a week for collaboration.

8. Final CTA & Important Links

🔥 Urgent Notice: Roles requiring niche skills like Databricks + Vector DBs are rare and highly competitive.

👉 APPLY NOW : Official Link

📢 Pro Tip: "In your cover letter, mention that you are excited about 'applying data engineering to improve patient outcomes.' It shows you care about their mission, not just the code."