Data Engineer, Translational Data Management, Automation & AI
At a glance
Mid-level Data Engineering role at Amgen. Hyderabad · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and operate end-to-end flows that ready biomarker and clinical records for analytics, visualization and machine-learning use
- Add automated checks, error handling and recovery processes to protect data quality and traceability
- Use Codex, agent-based automation and generative AI to meet turnaround and efficiency targets
- Bring new assays online with labs and vendors, setting data-transfer instructions and acceptance criteria
- Create dataset mapping and harmonization logic, prepare study analysis bundles, and maintain technical guides and specifications
What you bring
- A bachelor’s degree in a related listed field and at least eight years of overall experience
- At least three years in data or platform engineering, with strong Python programming and database design skills
- Practical experience with Databricks, orchestration tools, and agentic automation or AI workflow development
- Experience working with biomarker assays, clinical data, and data standards or harmonization methods
Who this fits
This hands-on engineering role suits someone who can work with computational biology, translational science, lab operations and external vendors. You’ll work from Amgen’s Hyderabad office.
From the employer
Career Category
ClinicalJob Description
Location: Amgen India office, Hyderabad
Employment type: Full-time
Department / Team: Computational Biology team, Precision Medicine
High-level role
We are seeking a hands-on, technically strong Translational Data Management, Automation, & AI Engineer to design, build, and operate robust biomarker and clinical data ingestion pipelines that feed our biomarker platform. You will work closely with computational biologists, translational scientists, data scientists, lab operations, and external vendors/contract research organizations (CROs) to ensure timely, accurate, and standardized ingestion of assay and clinical data for analysis, visualization, and machine-learning use cases supporting clinical trials.
Key responsibilities
Design, implement, test, deploy, and maintain end-to-end data ingestion pipelines that prepare biomarker and clinical data for downstream analytics, visualization, and ML models.
Implement automated data validation, quality control checks, error handling, and remediation workflows to ensure data quality and traceability.
Integrate Codex workflows, agentic automation and generative AI to meet TAT and efficiency goals.
Collaborate with internal biomarker labs and CROs/vendors to onboard new assays; author and maintain data transfer specifications, interface control documents, and acceptance criteria.
Build and maintain harmonization and mapping logic (units, controlled terminology, ontologies) and data models needed to standardize biomarker and clinical datasets.
Generate study-specific analysis bundle per request in defined timeline.
Produce and maintain clear documentation: software specification forms, data definition tables, runbooks, and onboarding guides.
Write clean, tested, maintainable Python code and contribute to CI/CD pipelines, automated testing, and release processes.
Required qualifications
Education & experience
8+ years of experience with Bachelor’s in Computational Biology, Bioinformatics, AI, Computer Science, Data Engineering, or related field. PhD is a plus.
3+ years of experience in data engineering or platform engineering roles; experience working with biomarker/biological/clinical data or in a clinical research environment is highly desirable.
Technical skills
Experience working with clinical labs, biomarker assays (immunoassay, flow cytometry, immunohistochemistry, proteomics, whole genome sequencing, exome sequencing, RNA-seq, methylation, metabolomics)
Strong programming skills in Python and database design. Experience with Databricks
Experience with workflow/orchestration tools (e.g., Airflow, Nextflow, snakemake).
Experience with agentic automation and formulation of AI workflow development and deployment, agentic automation tools and Codex workflows.
Familiarity with HPC, cloud platforms and storage (e.g., AWS) and best practices for secure data handling.
Experience with version control (Git), CI/CD, containerization (Docker)
Knowledge of clinical data formats and standards (e.g., CDISC/SDTM/ADaM).
Familiarity with data standardization and harmonization frameworks, controlled vocabularies
Experience building, testing and debugging R pipelines for production data processing.
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