Translational Data Management, Automation, & AI Engineer
About this opportunity
Career Category
Clinical
Job 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
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).
Experience working with clinical labs, biomarker assays (immunoassay, flow cytometry, immunohistochemistry, proteomics, whole genome sequencing, exome sequencing, RNA-seq, methylation, metabolomics)
Familiarity with data standardization and harmonization frameworks, controlled vocabularies
Experience building, testing and debugging R pipelines for production data processing.
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Job details
How this role compares
Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.
We currently track 383 comparable Drug Discovery & Preclinical Research roles across 47 biopharma companies.
Salary context
137 of 383 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 383 comparable roles
+ 10 more countries
Seniority mix
297 of 383 peers have a known seniority level
Therapeutic area mix
54 of 383 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.
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.