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Roche

MLOps Engineer (Focusing AWS platform) - RDT Pharma Medicines

Roche is hiring a MLOps Engineer (Focusing AWS platform) - RDT Pharma Medicines in Hyderabad, India. Level rates it ; you can apply on Roche.

Level's read on this job

  • Level rates this role Little AI ●○○○: aI is not part of the work.
  • The posting mentions Python, PySpark and SQL among its tools and skills.
  • Level classes it as a senior role.
  • Roche has 1,200+ other open roles on Level.
  • Posted within the past week.

AI in this role

Design and maintain scalable AWS data pipelines and data products supporting analytics and data science teams.

PythonPySparkSQLSparkAWS
ml-opsdata-engineeringetldata-pipelinesdata-warehousing

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

Job description

Data Engineer is responsible for designing, developing, and maintaining scalable and robust AWS based data pipelines and infrastructure. This role involves independently building ETL/ELT processes, optimizing data storage solutions (such as data warehouses , data lakes and Lake House ), ensuring data quality and reliability, and monitoring data systems. You will collaborate closely with data product owners, data scientists and data and business analysts to meet their specific data requirements, utilizing strong programming skills in Python, PySpark, expert SQL knowledge, and proficiency in big data technologies like Spark. Turn raw commercial data (claims, sales, specialty pharmacy, payer, CRM) into reliable, compliant, analytics-ready data products that power sales force effectiveness, market access, brand analytics and forecasting. Works independently, owns projects end to end, and partners with data product owners, data scientists and business analysts.


Job Responsibilities

  • Scope: Leads the design, build, and maintenance of scalable data pipelines. Works independently to execute specific data engineering projects from start to finish.
  • Accountability/Problem Solving: Solves complex data ingestion and processing problems. Optimizes data flows and actively enhances overall system performance.
  • Stakeholder Management: Engages directly with broader business units, business analysts and data scientists to thoroughly understand and meet their data needs. Communicates effectively with both technical and non-technical stakeholders.
  • Impact/Strategy: Handles big-sized data engineering projects. Implements robust strategies that significantly improve the organization's data infrastructure.
  • Complexity : Manages complex, large-scale data systems. Integrates multiple diverse data sources and ensures efficient, reliable data flow across platforms


Qualifications

  • 6-10 years in data engineering with proven independent delivery of large-scale pipelines.
  • Demonstrated experience handling big-sized data engineering projects and managing complex, large-scale data systems.
  • Proven track record of independently building ETL/ELT processes and optimizing data storage solutions such as data warehouses and data lakes.
  • Experience ensuring data quality, reliability, and monitoring data systems

Technical Skills

  • Proficiency in programming languages such as SQL, Python, PySpark
  • Proficiency in AWS Cloud based Data Engineering skills - S3, Lambda, AWS Glue, Athena, Airflow, DAGs, RedShift, RDS, AWS DataSync, AWS Step Functions, QuickSight, dbt
  • Strong, expert knowledge of cloud platforms and Big Data tools, specifically Spark.
  • Strong data modeling (dimensional, data vault, lakehouse), partitioning and file formats.
    Data quality, monitoring and incident troubleshooting experience
  • Experience building data products, designing a semantic layer, and handling unstructured data
  • Terraform or CloudFormation, Git-based CI/CD, containerization.
  • Advanced capability to solve complex data ingestion and processing problems, and to optimize data flows for enhanced system performance.


Preferred Qualifications

  • Exceptional communication and collaboration skills, with the proven ability to engage effectively with both technical audiences (like data scientists) and non-technical business stakeholders
  • HCP/HCO master data (Veeva Network, OneKey), patient-level longitudinal data, de-identification.
  • Commercial use cases: sales performance, market access, patient journey, next best action
  • AWS certification (Data Engineer Associate, Solutions Architect, Big Data Specialty).

#Hyderabad2026

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.


Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.

How we rate this

MLOps Engineer (Focusing AWS platform) - RDT Pharma Medicines at Roche rates 20 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsData EngineeringETLData PipelinesData WarehousingPythonPySparkSQL

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Tell me about a project where data engineering was part of your work. What did you do?
  3. Tell me about a project where etl was part of your work. What did you do?
  4. Tell me about a project where data pipelines was part of your work. What did you do?
  5. Tell me about a project where data warehousing was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: ML Ops, Data Engineering, ETL, Data Pipelines, and Data Warehousing. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.

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