Genetics Data Engineer – Merck KGaA

Genetics Data Engineer positions at top-tier global science and technology companies like Merck KGaA offer a unique opportunity to bridge the gap between complex biological data and life-saving healthcare solutions. We are currently seeking a high-caliber professional to join the Healthcare R&D team in Bangalore as a Senior Principle Data Engineer specializing in human quantitative genetics.

Role Overview

As a Genetics Data Engineer, you will be at the forefront of the company’s human quantitative genetics strategy. Your primary mission is to provide the foundational data engineering infrastructure that enables downstream analysis by data scientists and geneticists. You will ensure that data availability and quality never become bottlenecks for critical research.

This role requires a visionary approach to building FAIR (Findable, Accessible, Interoperable, and Reusable) data environments across both internal platforms and external Trusted Research Environments (TREs).

Responsibilities

1. Pipeline Orchestration: Develop and execute reproducible analysis pipelines using workflow managers like Nextflow, WDL, or Snakemake.

2. Data Integration: Ingest and maintain connections to world-class genetic reference databases such as OpenTargets and the GWAS Catalog.

3. TRE Management: Manage tools, containers (Docker/Singularity), and reference data within environments like the UK Biobank Research Analysis Platform.

4. AI Integration: Contribute to the design of agentic AI workflows to automate genetic evidence generation.

5. Performance Optimization: Optimize query performance to support rapid target assessments (~20-25 targets per year).

Also Check: Microbiologist II – AbbVie

Qualifications

1. Education: A Bachelor’s or Master’s degree in Computer Science, Data Engineering, or Bioinformatics.

2. Experience: A minimum of 5 years in data engineering with significant exposure to genomics (VCF, PLINK, BGEN formats).

3. Technical Skills: Deep expertise in Python and R, cloud-native compute (AWS/GCP/Azure), and genomics-specific tools like Hail and bcftools.

4. Vision: A solid understanding of data modeling, versioning, and medical data privacy requirements.