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Genetics Python Genomics Jobs (NOW HIRING)

Trans Ova Genetics

Sr. Data Engineer

Madison, WI

$115K - $138K/yr

Model and curate subject areas for genetics, reproduction, and bioinformatics (e.g., animals ... Apply strong programming skills in Python, SQL, and Spark to build scalable data and bioinformatics ...

Ohalo

Quantitative Geneticist

San Francisco, CA · On-site +1

You will be at the nexus of genetics, data science, and engineering, designing the predictive ... Genomic Prediction & GWAS: Design, build, and validate the primary statistical models (e.g., GBLUP ...

Ohalo

Quantitative Geneticist

South San Francisco, CA · On-site +1

You will be at the nexus of genetics, data science, and engineering, designing the predictive ... Genomic Prediction & GWAS: Design, build, and validate the primary statistical models (e.g., GBLUP ...

Ohalo

Quantitative Geneticist

South San Francisco, CA · On-site

You will be at the nexus of genetics, data science, and engineering, designing the predictive ... Genomic Prediction & GWAS: Design, build, and validate the primary statistical models (e.g., GBLUP ...

Baylor Genetics

Sr HPC Engineer

Houston, TX · On-site

$96K - $132K/yr

... genomic data platforms. This role will design, build, and operate scalable, high-throughput ... Genetics Compliance Program. • Perform other duties as assigned to support team and ...

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How much do genetics python genomics jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for genetics python genomics in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What are Genetics Python Genomics jobs?

Genetics Python Genomics jobs involve using Python programming to analyze and interpret genetic and genomic data. Professionals in this field work on tasks such as processing DNA sequences, identifying genetic variations, and developing bioinformatics tools to support research in genetics. The role often requires a strong background in biology, genetics, and computer science, particularly in data analysis and software development. These jobs are typically found in research institutions, biotech companies, and healthcare organizations working on precision medicine and genetic research.

How do Genetics Python Genomics professionals typically collaborate with biologists and data scientists in research projects?

Genetics Python Genomics professionals often work closely with biologists to understand experimental goals and interpret genetic data, while also partnering with data scientists to develop and implement robust computational workflows. Collaboration usually involves translating biological questions into analytical tasks, creating and optimizing Python scripts for genomics data processing, and presenting results in a clear, actionable format. Effective communication and interdisciplinary teamwork are key in this role, as projects frequently require integrating knowledge from genetics, programming, and statistics to drive scientific discovery.

What is the difference between Genetics Python Genomics vs Bioinformatics Analyst?

AspectGenetics Python GenomicsBioinformatics Analyst
Required CredentialsBachelor's or Master's in Genetics, Bioinformatics, or related field; proficiency in PythonBachelor's or Master's in Bioinformatics, Computer Science, or related field; programming skills in Python and R
Work EnvironmentResearch labs, biotech companies, academic institutionsResearch institutions, healthcare, biotech firms, data analysis teams
Industry UsageGenetics research, genomics data analysis, personalized medicineGenomics, disease research, data interpretation, software development

Genetics Python Genomics focuses on developing Python-based tools for genetic data analysis, often within research or biotech settings. Bioinformatics Analysts also work with genetic data but typically perform broader data interpretation and reporting tasks. Both roles require programming skills and a background in biology or genetics, but Genetics Python Genomics emphasizes coding and tool development, while Bioinformatics Analysts focus on data analysis and insights.

What are the key skills and qualifications needed to thrive as a Genetics Python Genomics specialist, and why are they important?

To excel as a Genetics Python Genomics specialist, you need a solid background in genetics, bioinformatics, and proficiency in Python programming, often supported by a relevant degree such as bioinformatics, computational biology, or genetics. Familiarity with bioinformatics tools (e.g., Biopython, GATK, PLINK), next-generation sequencing platforms, and genomic databases is essential. Strong analytical thinking, attention to detail, and effective communication help in interpreting complex data and collaborating across multidisciplinary teams. These competencies are vital for accurately analyzing genomic data, developing reproducible workflows, and advancing research or clinical applications.
Infographic showing various Genetics Python Genomics job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $121,932 per year, or $58.6 per hour.
Sr. Data Engineer

Sr. Data Engineer

Trans Ova Genetics

Madison, WI

$115K - $138K/yr

Full-time

Posted 3 days ago

Job description

This role is responsible for the design, development, and maintenance of data integration, analytics, and reporting solutions that support our animal genetics and bioinformatics workloads. The ideal candidate will possess expertise in Databricks and modern data engineering tools such as Azure Data Factory, combined with hands on experience working with biological, genomic, or other omics datasets. This position requires a proactive, self-motivated, and results-oriented individual with a passion for data, a strong understanding of data architecture and warehousing principles, and an appreciation for bioinformatics workflows in a commercial genetics environment. 

Responsibilities 

Data Integration 

  • Design, develop, and maintain robust and efficient ETL/ELT pipelines and processes on Databricks for both operational and bioinformatics datasets (e.g., genomic markers, phenotypic data, laboratory outputs). 

  • Ingest, transform, and harmonize structured and semi-structured biological data from lab systems, LIMS, sequencing platforms, and external partners into the enterprise data platform. 

  • Troubleshoot and resolve Databricks pipeline errors and performance issues. 

  • Optimize data flow performance and minimize data latency across scientific and business use cases. 

  • Implement data quality checks, validations, and reconciliation processes within ETL workflows, including domain-specific checks for genomic and phenotypic data. 

Databricks Development 

  • Develop and maintain Databricks pipelines, notebooks, and datasets using Python, Spark, and SQL. 

  • Optimize Databricks jobs for performance and cost-effectiveness, including largescale bioinformatics and analytics workloads. 

  • Integrate Databricks with other data sources and systems, including lab instruments, genomic databases, and on-prem or cloud data stores. 

  • Participate in the design and implementation of data lake architectures that support both traditional analytics and bioinformatics pipelines. 

Data Warehousing 

  • Participate in the design and implementation of data warehousing solutions to support reporting, analytics, and scientific modeling. 

  • Model and curate subject areas for genetics, reproduction, and bioinformatics (e.g., animals, pedigrees, genotypes, breeding values, trials). 

  • Support data quality initiatives and implement data cleansing procedures across business and scientific domains. 

Reporting and Analytics 

  • Collaborate with business users, scientists, geneticists, and bioinformaticians to understand data requirements for department-driven reporting and analytics needs. 

  • Maintain and extend the existing library of complex dashboards and visualizations to surface genetic, reproductive, and operational insights. 

  • Enable self-service analytics for R&D and product teams by exposing well- governed, documented data products. 

  • Troubleshoot and resolve report issues, including performance bottlenecks and data inconsistencies. 

Cloud Platform Experience 

  • Apply strong programming skills in Python, SQL, and Spark to build scalable data and bioinformatics workflows. 

  • Use CI/CD and IaC tools (Terraform, ARM, CloudFormation) to automate deployment of data platform components and analytics environments. 

  • Design and implement Databricks platform architecture on Azure and AWS infrastructure, including environments that support largescale scientific computation. 

  • Contribute to cloud security, governance, and cost optimization practices for data and bioinformatics workloads. 

Bioinformatics and Scientific Collaboration 

  • Partner with geneticists, biostatisticians, and bioinformaticians to translate scientific requirements into scalable data and platform architectures. 

  • Support or orchestrate bioinformatics pipelines (e.g., variant processing, quality control, annotation, genotype imputation, genomic evaluation) using cloud and Databricks capabilities. 

  • Ensure that data models, pipelines, and storage structures meet the needs of downstream analytics, predictive models, and genetic evaluations. 

  • Advocate for best practices in managing sensitive biological and genetic data, including data governance, access control, and compliance with relevant standards and regulations. 

Collaboration and Communication 

  • Thrive in an entrepreneurial, self-starting, and fast-paced environment, working both independently and with our highly skilled teams. 

  • Collaborate effectively with business users, data analysts, scientists, and other IT teams. 

  • Communicate technical information clearly and concisely, both verbally and in writing, to technical and nontechnical stakeholders. 

  • Document all development work, data models, and procedures thoroughly, including bioinformatics and scientific data flows. 

Continuous Growth 

  • Keep abreast of the latest advancements in data integration, cloud platforms, bioinformatics tooling, and data engineering technologies. 

  • Continuously improve skills and knowledge through training and self-learning in both data engineering and bioinformatics domains. 

Requirements 

  • Bachelor's degree in Computer Science, Information Systems, Bioinformatics, Computational Biology, or a related field; a Master's degree is an asset. 

  • 7+ years of experience in data integration and reporting, with experience designing and operating cloud-based data platforms. 

  • Extensive experience with Databricks, including Python, Spark, and Delta Lake. 

  • Strong proficiency with relational databases (e.g., SQL Server, RDS), including TSQL, stored procedures, and functions. 

  • Experience with data warehousing concepts and best practices. 

  • Experience with Microsoft Azure cloud platform; exposure to Microsoft Fabric is desirable. 

  • Hands on experience working with biological, genomic, or other omics datasets in a bioinformatics or life sciences setting (e.g., sequence data, SNP arrays, GWAS outputs, phenotypic traits). 

  • Familiarity with common bioinformatics tools, data formats (e.g., FASTQ, VCF, PLINK), and workflows is highly desirable. 

  • Strong analytical and problem-solving skills, with the ability to reason about complex data and scientific requirements. 

  • Excellent communication and interpersonal skills. 

  • Ability to work independently and as part of a cross-functional team across IT, science, and business. 

  • Experience with Agile methodologies. 

  • Demonstrated background in bioinformatics or computational biology, preferably supporting genetics, breeding, or life science research in an applied or commercial context. 

  • Must be legally authorized to work in the United States.

As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry - Alta Genetics, GENEX, Genetics Australia, Leachman Cattle, Jetstream, PEAK, SCCL, Trans Ova Genetics and VAS.  Each organization has its unique identity, products, and services. These companies work globally to provide cutting-edge dairy and beef genetics, customized reproductive services to maximize conceptions, dairy management information to take producers to the frontline of progressive dairy farming, and an array of products and services to help bovines reach their full genetic potential. URUS has 9 brands in 17 retail countries and employs nearly 2,800 people globally.


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