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Omer AcarData engineering and computational biology consultingBook a discovery call

Data engineering and computational biology consulting

Turn complex biotech data into systems your teams can use.

I help biotech and pharmaceutical teams structure fragmented data, automate scientific workflows, and build tools for research and clinical data exploration.

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Selected outcomes

of research and clinical data
5+ TB
processing-time reduction
75%
of sequencing and experimental data
10+ TB

Services

How I help biotech teams

Focused technical support for teams working with complex scientific, clinical, and research data.

Interactive Scientific Dashboards

Scientist-friendly dashboards for exploring genomic, clinical, and research data.

  • R Shiny or Streamlit interfaces tailored to scientific teams
  • Integration with Snowflake, Amazon S3, or Parquet-based pipelines
  • Usability and reproducibility for scientific data exploration

End-to-End Data Workflows

Pipelines that turn raw scientific outputs into structured, analysis-ready data.

  • Workflows from raw lab outputs to cloud data platforms
  • Parsing, transformation, and validation for PDFs, spreadsheets, LIMS, or APIs
  • Implementation with Python, Dagster, AWS Lambda, or dbt

AI-Assisted Document Processing

A capability for extracting structured scientific data from unstructured documents.

  • Experiment metadata extraction from PDF, Word, or plaintext sources
  • Curated and validated outputs for loading into data platforms
  • Scientific text analysis using AWS Bedrock and related tools

Collaborative Consulting

Flexible technical collaboration with bioinformatics, data, and translational teams.

  • Project-based, part-time, or embedded support
  • Direct collaboration with scientific and technical teams
  • Support for pipelines, dashboards, and scientific data workflows

Experience

Scientific context. Engineering discipline.

Experience connecting scientific requirements with production-grade data engineering.

Sarepta Therapeutics

Bioinformatics consulting and data engineering for production genomics and clinical data platforms.

  • Built systems supporting variant annotation, omics integration, and downstream analysis across 5+ TB of research and clinical data.
  • Developed scalable ingestion, validation, harmonization, and analysis pipelines that reduced processing time by 75%.
  • Collaborated with computational biology, bioinformatics, clinical, immunology, and engineering teams.

University of Pittsburgh

Graduate research in computational biology and reproducible multi-omics analysis.

  • Designed genomics and multi-omics workflows using Python, R, SQL, and Snakemake for 10+ TB of sequencing and experimental data.
  • Collaborated with interdisciplinary research teams and co-authored peer-reviewed publications.

Working together

Flexible by design

Choose the level of support that fits the problem, timeline, and team.

Project-based consulting

Focused support for a defined scientific data challenge or delivery.

Part-time consulting

Ongoing technical support that adapts to a team’s changing priorities.

Embedded collaboration

Direct collaboration with bioinformatics, data, and translational teams.

Scientific credibility

Research foundation

PhD in Computational Biology from Carnegie Mellon University and the University of Pittsburgh, with peer-reviewed work spanning genomics, evolution, and computational biology.

View Google Scholar
  1. Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome2024
  2. A vast evolutionarily transient translatome contributes to phenotype and fitness2023
  3. De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences2020

Working stack

PythonRSQLShinyStreamlitReact and Next.jsAWSSnowflakeDagsterDocker and KubernetesAWS BedrockDocument processing
Omer Acar

About

About Omer

I’m a data engineer and computational biologist with a PhD in computational biology. I build pipelines and tools that make complex genomic data easier for scientists to explore, interpret, and act on.

My work spans variant interpretation dashboards in R Shiny, RNA-seq pipelines, cloud deployments for bioinformatics tools, and scientific data workflows.

AI and large language models are part of my current toolkit for scientific text analysis and structured data extraction, alongside the validation needed to maintain scientific accuracy.

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Have a scientific data challenge in mind?

Let's make the complex usable.

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