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Forum: Transitioning from bioinformatics to data engineering - advice needed

I have an MSc in Medical Epigenomics and ~3 years of experience in bioinformatics (Python/R, NGS analysis, Nextflow pipelines, HPC). I'm considering moving into data engineering or workflow development roles.

My skills: Python (3+ yrs), R (3+ yrs), Bash, Nextflow (certified), Docker, Git, FAIR principles. SQL is basic – I'm learning.

Questions:

What additional skills should I focus on for data engineering (e.g., cloud, advanced SQL, Airflow)?

Are there entry-level data engineering roles that value bioinformatics backgrounds?

How should I position my Nextflow/pipeline experience for non-bioinformatics roles?

Has anyone here made a similar transition? What was your path?

Thanks!

career transition entrylevel
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updated by Abdullah - NotchBio • Abdullah - NotchBio • written by eyaggelia146 • eyaggelia146

Can you also add what your definition of data engineeing is? You seem to have a solid background already and sometimes this may be a matter of semantics.

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written by GenoMax GenoMax

From what I understand is the use of coding structure and review in order to build and maintain databases. In some cases it also involves data analysis and documentation.

From my initial research I still need to learn SQL and AWS

Im still looking into it that's why I asked for the opinion of someone that has done this transition

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written by eyaggelia146 • eyaggelia146

2 answers

The missing link is advanced statistics. Get certified in advanced statistical methods e.g. GLMs etc. to blossom into a data scientist.

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written by himanshu0102 • himanshu0102

OP is asking about data engineering which is not the same thing as a data scientist. It seems to be their intention to leave bioinformatics, if the opportunity is right.

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written by GenoMax GenoMax

Yes you are right. I am looking to see if i can pivot to data engineering or data analysis

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written by eyaggelia146 • eyaggelia146

Even so, I'd say statistics would be a good addition for any domain centered on data. Even if not immediately applicable, a candidate with an understanding of statistics has more credibility.

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written by Istvan Albert Istvan Albert

I would like more information on that. I just started exploring other options so I am open to learning about other directions. To become a data scientist I would just need the advanced statistics certificate?

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written by eyaggelia146 • eyaggelia146

Honestly, I think you're closer to data engineering than you might realize.

A lot of what you've listed like nextflow, Docker, HPC environments, reproducible pipelines, Git, automation are already very relevant skills. The domain is different, but building and maintaining data pipelines is a big part of data engineering as well.

If I were in your position, I'd focus on three things:

  1. SQL (definitely beyond the basics)
  2. Cloud platforms (AWS seems to be the most requested, though Azure and GCP are also common)
  3. Orchestration tools like Airflow

I'd also spend some time learning data warehousing concepts, ETL/ELT patterns, and working with larger structured datasets.

When talking to non-bioinformatics employers, I'd frame Nextflow experience as workflow orchestration, pipeline automation, dependency management, containerized deployments, and processing large-scale datasets rather than focusing too much on the biology itself.

I've seen quite a few bioinformaticians move into platform engineering, MLOps, workflow engineering, and data engineering because the underlying mindset is actually pretty similar.

As for data science, I don't think an advanced statistics certificate alone would make someone a data scientist. Statistics is important, but most data science roles also expect machine learning, experimentation, feature engineering, model evaluation, and business/domain understanding. It's a different path from data engineering, although there is definitely overlap.

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written by Abdullah - NotchBio • Abdullah - NotchBio

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