At The Dna Group, a Software Engineer plays a pivotal role at the intersection of advanced technology, biotechnology, and high-throughput manufacturing. Unlike traditional software roles that focus solely on web applications or isolated cloud services, engineering here requires building software that interacts with physical systems, automation hardware, and complex scientific workflows. Your work directly impacts the speed, accuracy, and scalability of biotechnology manufacturing pipelines, transforming complex biological processes into reproducible, software-driven operations. This role is highly collaborative and interdisciplinary. You will design, develop, and maintain software systems that control laboratory instrumentation, manage massive datasets generated by genomic sequencing, and optimize manufacturing execution systems. Because The Dna Group relies on seamless integration between physical machinery and digital control loops, your code will have a direct, visible impact on physical production lines in facilities from Coralville, IA to global hubs. To succeed in this position, you must be comfortable working alongside Sr. Process Engineers, biologists, and automation specialists. The challenges you will solve are highly complex, requiring a deep appreciation for core engineering principles, system reliability, and real-time data processing. It is an inspiring environment for engineers who want their software to drive tangible, physical advancements in biotech.
Phone Screening
reported30-minute conversation with a recruiter or hiring manager to discuss your background and interest in the role.
What to demonstrate
- 30-minute conversation with a recruiter or hiring manager to discuss your background and interest in the role
- Depth in Programming Fundamentals
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Assessment
reportedFollow-up interview featuring basic coding questions and scenario-based discussions emphasizing practical coding.
What to demonstrate
- Follow-up interview featuring basic coding questions and scenario-based discussions emphasizing practical coding
- Depth in Programming Fundamentals
How to prepare
- Answer aloud and timed: If a manufacturing execution system (MES) experiences a database write delay, how would you prevent the physical assembly line from failing or producing defective units?
- Answer aloud and timed: What core engineering principles do you prioritize when designing software that must run continuously in a manufacturing facility?
Panel Interview
reportedIntensive 4 to 6-hour interview with a panel of 3 to 5 engineers, focusing on guided technical questions and behavioral discussions.
What to demonstrate
- Intensive 4 to 6-hour interview with a panel of 3 to 5 engineers
- Focusing on guided technical questions and behavioral discussions
How to prepare
- Answer aloud and timed: Write a function to parse and analyze large streams of genomic sequence data, optimizing for memory usage.
- Answer aloud and timed: How would you implement a thread-safe queue to handle commands being sent from a control server to multiple laboratory instruments?
PracHub editorial advice for the preparation topics above.
Understand the physical context
Whenever you are asked a technical or system design question, always consider the physical constraints of a manufacturing environment. Ask yourself how latency, hardware failures, or network drops would impact the physical production line.
Communicate your thought process
During the panel interview, the engineers are highly collaborative and will guide you through technical challenges. Speak out loud as you solve problems so they can understand your approach and offer guidance.
Prepare cross-functional stories
Be ready with behavioral examples that highlight your ability to collaborate with non-technical stakeholders. Emphasize how you gathered requirements, handled differing perspectives, and delivered a successful product.
Show genuine curiosity
Ask insightful questions about the company's manufacturing pipelines, the specific challenges their automation teams face, and how they manage data flow between physical labs and software systems.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to parse and analyze large streams of genomic sequence data, optimizing for memory usage.
Write a function to parse and analyze large streams of genomic sequence data, optimizing for memory usage.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
How would you implement a thread-safe queue to handle commands being sent from a control server to multiple la
How would you implement a thread-safe queue to handle commands being sent from a control server to multiple laboratory instruments?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Given a dataset of manufacturing process logs, write a script to identify anomalies or bottlenecks in the prod
Given a dataset of manufacturing process logs, write a script to identify anomalies or bottlenecks in the production timeline.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Explain how you would optimize a slow-running database query that tracks inventory levels of biological reagen
Explain how you would optimize a slow-running database query that tracks inventory levels of biological reagents.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
How would you design a software interface to monitor and control a high-throughput bioreactor in real-time?
How would you design a software interface to monitor and control a high-throughput bioreactor in real-time?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
If a manufacturing execution system (MES) experiences a database write delay, how would you prevent the physic
If a manufacturing execution system (MES) experiences a database write delay, how would you prevent the physical assembly line from failing or producing defective units?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
What core engineering principles do you prioritize when designing software that must run continuously in a man
What core engineering principles do you prioritize when designing software that must run continuously in a manufacturing facility?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics The Dna Group candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the The Dna Group loop
- Write out the reported sequence: Phone Screening, Technical Assessment, Panel Interview.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Programming Fundamentals
- Spend the session on Programming Fundamentals, which The Dna Group candidates report being tested on.
- Write one worked example in Programming Fundamentals and time yourself on it.
Deliverable: One timed worked example in Programming Fundamentals.
03Work Coding Interview Problem Solving
- Spend the session on Coding Interview Problem Solving, which The Dna Group candidates report being tested on.
- Write one worked example in Coding Interview Problem Solving and time yourself on it.
Deliverable: One timed worked example in Coding Interview Problem Solving.
04Work Technical Discussion / Communication
- Spend the session on Technical Discussion / Communication, which The Dna Group candidates report being tested on.
- Write one worked example in Technical Discussion / Communication and time yourself on it.
Deliverable: One timed worked example in Technical Discussion / Communication.
05Answer out loud: Domain & Scenario-Based Questions
- Answer aloud, timed: How would you design a software interface to monitor and control a high-throughput bioreactor in real-time?
- Answer aloud, timed: Describe a time when you had to write software that interacted directly with physical hardware or sensors. How did you handle latency and data loss?
Deliverable: Spoken answers to 2 reported Domain & Scenario-Based Questions question(s), under time.
06Answer out loud: Coding & Problem-Solving
- Answer aloud, timed: Write a function to parse and analyze large streams of genomic sequence data, optimizing for memory usage.
- Answer aloud, timed: How would you implement a thread-safe queue to handle commands being sent from a control server to multiple laboratory instruments?
Deliverable: Spoken answers to 2 reported Coding & Problem-Solving question(s), under time.
07Answer out loud: Behavioral & Team Collaboration
- Answer aloud, timed: Tell me about a time you had to collaborate with non-technical stakeholders, such as laboratory scientists or process engineers, to gather system requirements.
- Answer aloud, timed: How do you handle a situation where your software functions perfectly in a simulation, but fails when deployed to the physical production floor?
Deliverable: Spoken answers to 2 reported Behavioral & Team Collaboration question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe a time when you had to write software that interacted directly with physical hardware or sensors. How
Describe a time when you had to write software that interacted directly with physical hardware or sensors. How did you handle latency and data loss?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time you had to collaborate with non-technical stakeholders, such as laboratory scientists or
Tell me about a time you had to collaborate with non-technical stakeholders, such as laboratory scientists or process engineers, to gather system requirements.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle a situation where your software functions perfectly in a simulation, but fails when deployed
How do you handle a situation where your software functions perfectly in a simulation, but fails when deployed to the physical production floor?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Why are you interested in working at the intersection of software engineering and biotechnology at The Dna Gro
Why are you interested in working at the intersection of software engineering and biotechnology at The Dna Group?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a time you had to debug a complex issue under tight deadlines. How did you maintain quality and commu
Describe a time you had to debug a complex issue under tight deadlines. How did you maintain quality and communicate progress to your team?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Describe a time when you had to write software that interacted directly with physical hardware or sensors. How did you handle latency and data loss?
- 02
Tell me about a time you had to collaborate with non-technical stakeholders, such as laboratory scientists or process engineers, to gather system requirements.
- 03
How do you handle a situation where your software functions perfectly in a simulation, but fails when deployed to the physical production floor?
- 04
Why are you interested in working at the intersection of software engineering and biotechnology at The Dna Group?
How technical are the coding assessments at The Dna Group?
The coding questions focus on practical software engineering rather than abstract, highly complex algorithmic puzzles. You should be prepared to solve standard data structure and string manipulation problems, with a heavy emphasis on write-time efficiency, readability, and error handling.
The Dna Group Software Engineer candidate reports ↗Do I need a background in biology or biotechnology to apply?
No, a formal degree in biology is not required. However, you must demonstrate a strong interest in the domain and a willingness to learn. Showing that you understand how software integrates with physical manufacturing and laboratory processes is highly valued.
The Dna Group Software Engineer candidate reports ↗What is the team structure like for Software Engineers?
The engineering teams are typically small, focused, and highly collaborative. You will work closely with other software engineers, hardware specialists, and process engineers, allowing you to take significant ownership of your projects and see the direct impact of your work. Some candidates have reported communication delays during the post-interview follow-up stage. Be prepared to proactively follow up with your recruiter if you do not receive updates within the expected timeframe.
The Dna Group Software Engineer candidate reports ↗What is the typical duration of the entire interview process?
The process generally takes between three to six weeks from the initial phone screen to the final decision. The timeline can vary depending on scheduling for the intensive, multi-hour panel interview.
The Dna Group Software Engineer candidate reports ↗What topics does The Dna Group test in interviews?
The Dna Group interviews most often cover SDLC Methodologies, Product Management, Account Executive Sales Process, Programming Fundamentals, and Technical Communication (In-person Talk). The exact emphasis depends on the specific role you apply for.
The Dna Group Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01The Dna Group Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Discord Software Engineer Interview Guide
Database practice for Discord candidates: how an index changes a query plan, and which anomalies survive a given isolation level, since read committed still permits non-repeatable reads.
Rystad Energy Software Engineer Interview Guide
Prepare for Rystad Energy software interviews with worked Python validation, OOP and decorator examples, plus an ingestion case and practical checks.
Zwift Software Engineer Interview Guide
What Zwift asks Software Engineer candidates, from reported rounds and questions.
Zumper Software Engineer Interview Guide
What Zumper asks Software Engineer candidates, from reported rounds and questions.