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Bioinformatics Data Engineer Jobs in Dallas, TX (NOW HIRING)

4431

Data Scientist II

Dallas, TX

Impacts data acquisition, management, and analysis in basic, translational, and clinical research ... Bioinformatics, (Bio)mathematics, (Bio)statistics, Physics, Electrical Engineering, or related ...

Exatech Inc

Azure Data Solutions Architect

Dallas, TX · On-site

$62.75 - $81.75/hr

Azure Data Solutions Architect Remote Xebia is in need of a leading technical contributor who can ... engineering, or bioinformatics/computational biology, with 4+ years of experience (or MS with 2+ ...

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All JobsBioinformatics Data Engineer JobsBioinformatics Data Engineer Jobs in Dallas, TX

Bioinformatics Data Engineer information

See Dallas, TX salary details

$42.5K

$129.6K

$235.9K

How much do bioinformatics data engineer jobs pay per year?

As of Jul 27, 2026, the average yearly pay for bioinformatics data engineer in Dallas, TX is $129,642.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,000.00 and $155,300.00 per year, depending on experience, location, and employer.

How do Bioinformatics Data Engineers typically collaborate with researchers and other teams in a biomedical organization?

Bioinformatics Data Engineers often work closely with biologists, data scientists, and software engineers to ensure the effective collection, processing, and analysis of complex biological data. They regularly participate in cross-functional meetings to understand research goals, develop data pipelines, and troubleshoot data-related issues. Collaboration is essential, as engineers must translate scientific requirements into technical solutions, provide data access and visualization tools, and support researchers in extracting meaningful insights from large datasets. This teamwork fosters a dynamic environment where communication and adaptability are key.

What is the difference between Bioinformatics Data Engineer vs Bioinformatics Analyst?

AspectBioinformatics Data EngineerBioinformatics Analyst
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fields; programming skillsBachelor's or Master's in Bioinformatics, Biology, or related fields; data analysis skills
Work EnvironmentData pipelines, database management, software developmentData interpretation, report generation, biological data analysis
Employer & Industry UsageBiotech companies, research labs, pharmaResearch institutions, healthcare, biotech
Common Search & ComparisonFocuses on data infrastructure and pipelinesFocuses on biological data interpretation

The main difference between a Bioinformatics Data Engineer and a Bioinformatics Analyst lies in their focus areas. Data Engineers build and maintain data pipelines and infrastructure, while Analysts interpret biological data to generate insights. Both roles require strong bioinformatics knowledge, but Data Engineers emphasize programming and data management, whereas Analysts focus on biological interpretation and reporting.

What is a Bioinformatics Data Engineer?

A Bioinformatics Data Engineer is a professional who designs, develops, and maintains data infrastructure for managing and analyzing large-scale biological data, such as genomics or proteomics datasets. They build pipelines and tools to process, store, and retrieve complex biological information efficiently. Their work enables researchers and scientists to access and interpret data for discoveries in fields like medicine, genetics, and biotechnology. Often, they collaborate closely with bioinformaticians, data scientists, and software engineers to support research initiatives.

What are the key skills and qualifications needed to thrive as a Bioinformatics Data Engineer, and why are they important?

To thrive as a Bioinformatics Data Engineer, you need a strong background in computer science, biology, and statistics, often supported by a relevant degree and experience in data engineering. Proficiency with programming languages (such as Python, R, or SQL), bioinformatics tools, cloud platforms, and big data frameworks (like Hadoop or Spark) is typically required. Strong problem-solving, collaboration, and communication skills help you work effectively across interdisciplinary teams and convey complex findings. These skills ensure accurate analysis, efficient data pipeline development, and meaningful insights that advance biological research and healthcare solutions.
What are popular job titles related to Bioinformatics Data Engineer jobs in Dallas, TX? For Bioinformatics Data Engineer jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Bioinformatics Data Engineer jobs in Dallas, TX look for? The top searched job categories for Bioinformatics Data Engineer jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Bioinformatics Data Engineer jobs? Cities near Dallas, TX with the most Bioinformatics Data Engineer job openings:
Bioinformatics Data Engineer jobs near you
Infographic showing various Bioinformatics Data Engineer job openings in Dallas, TX as of July 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $129,642 per year, or $62.3 per hour.
ML / Bioinformatics Data Scientist

ML / Bioinformatics Data Scientist

IT America Inc

Dallas, TX • Remote

Contractor

Posted 25 days ago

Job description

Position: ML / Bioinformatics Data Scientist

Location: Remote (PST work hours)

Duration: Long term contract

About the Role:

We are seeking a highly motivated and collaborative Bioinformatics/ML scientist to join the Computational biology & Medicine department in Computational Sciences COE (Center of Excellence) within Genentech’s Research and Early Development (gRED). The successful candidate will contribute to a cross-functional project that will apply Machine Learning (ML) models to multi-modal datasets collected from clinical trials. This role requires a deep understanding of application of Machine Learning models, a background in biology, a passion for innovation, and a commitment to improving healthcare outcomes through cutting-edge technology.

We are looking for exceptional researchers with a passion for interdisciplinary research and technical problem-solving, and a proven ability to develop and implement research ideas. The candidate is expected to have worked on previous ML modeling projects and applying them to multi-modal datasets to be considered.

About the Project:

The goal of this project is to develop a machine learning model to predict a patient's risk for drug-induced liver toxicity based on a wide variety of patient characteristics including clinical, genetics, omics and safety labs. The focus will be harmonizing these diverse data sources, deriving new features, and  building machine learning models designed to identify a predictive signature that can distinguish between at-risk and not-at-risk patient populations.

Key Responsibilities:

  • Data centralization and harmonization
  • Applying ML methods on assembled dataset to identify patients’ risk for drug-induced liver toxicity.
  • Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.

Educational Background:

  • PhD degree in quantitative field ( e.g., Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics) 

Experience:

  • Proven track record of working with statistical modeling techniques, including ML methods, is required
  • Demonstrated interest in problems across biology as applied to the discovery and development of treatments for disease is preferred

Technical Skills:

  • Data Science & Programming: Expertise in Python/R for data manipulation, statistical analysis, and ML model building (required)
  • Multimodal Data & Modeling: Proven ability to work with diverse data types (omics, clinical, imaging) (required).
  • Knowledge of statistics and experience with survival analysis (required)
  • Domain & AI-specific Skills: Experience with NLP/LLMs for feature extraction from unstructured text, and a strong background in a neuroscience (preferred)

Soft Skills:

  • Excellent communication, collaboration, and problem-solving skills (required).

Publications:

  • Strong publication record and experience contributing to research communities.