Bioinformatics Data Curation Engineer in US-Remote

  • Title: Bioinformatics Data Curation Engineer
  • Code: RCI-25564
  • RequirementID: 117230
  • Location: US-Remote
  • Posted Date: 04/18/2024
  • Duration: 12 Months
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  Job Description

Location/Site: LC or remote (Eastern time zone preferred) Hybrid if near LC, Remote otherwise

5+ years of experience



• Bachelor's degree in computer science, bioinformatics, or a related field +3 years of applicable experience.
• Experience with building and running workflows for RDMS data loading and ETL processes.
• Demonstrated experience building ETL environments from scratch.
• Familiarity with AWS ETL services (e.g., Glue, Athena).
• Proficient in PostgreSQL (or equivalent) and ability to write complex queries for data extraction and analysis.
• Strong programming skills in Python and R for scripting and automation.
• Familiarity with genomic data formats and databases commonly used in bioinformatics research.
• Knowledge of data modeling concepts and implementing common data models in a relational database.
• Familiarity with data cleaning, normalization, and quality control processes.
• Excellent communication skills and ability to collaborate with researchers and stakeholders.


  • We are looking for relatively broadly available technical skllls: Python, R, PostgreSQL, ETL. But these seem to be somewhat light relative to the overall experience profile of the candidates. Particularly wrt the ETL environments.
  • We’re in the initial stages of building a data ecosystem and need to bring in people who are capable of building ETL environments from scratch (as opposed to using pre-existing environments or solely AWS based services). 

EST or CST time zone will work

  • Tools: Python, R programming, Postgres
  • Bachelor’s degree will also work.
  • understanding how servers
  • 3 yrs exp needed minimum.
  • Linux environment exp will work.
  • Background in Postgres, python, R programming and bioinformatics and genomics data
  • Responsible for building and running workflows to load and manage high-value datasets in a centralized environment.
  • Establish centralized data.
  • Any genomics data exp will work.
  • Transcriptomic or structural data exp will work.
  • Phd with few yrs. exp will work.
  • MYSQL background or MariaDB exp will also work if the candidates doesn’t have Postgres exp
  • Not looking for automation process.
  • Types of data bases: statistics from data processing, real world data, internally generated data, any thing that supports bioinformatics data.

What are the top 3-5 skills, experience or education required for this position:

  • PostgreSQL
  • Bioinformatics datasets (BulkRNAseq, CRISPR)
  • Python/R
  • Common data models
  • Code management and documentation


  • As a Data Analyst in Client Genomics Research Center’s Bioinformatics Engineering team, you will be responsible for developing and running workflows to standardize and load project datasets in a centralized environment.
  • You will work closely with Bioinformatics Engineering, as well as therapeutic area facing bioinformatics research scientists to leverage common data models, and support GRC Bioinformaticians’ needs for loading and querying.
  • Your expertise in PostgreSQL for database management and Python and R for scripting and automation will be crucial in developing and maintaining ETL processes to ensure data quality and integrity.


  • Develop and maintain a functional understanding of the GRC common data models, loading processes, and requirements, and perform accurate and efficient loading of new and historical datasets into the GRC’s Omics Data Server.
  • Collaborate with Bioinformatics Engineers to develop and implement additional data loading workflows.
  • Partner with Bioinformatics research scientists to identify, process, and load project data into the common data models.
  • Build and execute ETL processes to integrate non-GRC generated high-value datasets into the common data models.
  • Keep thorough documentation for tracking datasets and loading tasks.
  • Ensure reproducibility and facilitate collaboration with team members by documenting and versioning code with git.

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