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DRD Recruiting Software Engineer Interview Guide

DRD Recruiting Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

The Senior PLC Software Engineer role at DRD Recruiting is a cornerstone position within the industrial automation sector. You will be instrumental in the design, development, and commissioning of sophisticated software for high-end automatic machinery. As a leader in global packaging solutions, our client relies on this role to bridge the gap between complex mechanical engineering and high-performance automation, ensuring that every machine operates with precision and efficiency. This role is not merely about writing code; it is about architectural influence and technical leadership. You will work in autonomy on critical projects while serving as a mentor to junior and mid-level engineers, effectively shaping the technical standards of the team.

This guide is scoped to a Software Engineer candidate at DRD Recruiting.

Focus areas

  • PLC programming
  • Industrial automation
  • Siemens TIA Portal

The DRD Recruiting interview process

DRD Recruiting candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Gauge your technical background and alignment with the company’s automation needs.

What to demonstrate

  • Gauge your technical background and alignment with the company’s automation needs
  • Depth in PLC programming

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 Deep-Dive

Discuss specific projects, experience with Siemens or Schneider ecosystems, and methodology for machine commissioning.

What to demonstrate

  • Discuss specific projects, experience with Siemens or Schneider ecosystems, and methodology for machine commissioning
  • Depth in PLC programming

How to prepare

  • Answer aloud and timed: What are the key differences you consider when programming for high-speed packaging machines versus general industrial plants?
  • Answer aloud and timed: How do you optimize PLC scan times for complex, multi-axis motion control tasks?

Problem-Solving Discussion

Engage in discussions that simulate the pressure of an on-site installation and explore your career trajectory.

What to demonstrate

  • Engage in discussions that simulate the pressure of an on-site installation and explore your career trajectory
  • Depth in PLC programming

How to prepare

  • Answer aloud and timed: Can you explain your process for integrating HMI/SCADA systems with existing PLC architecture?
  • Answer aloud and timed: How do you handle a situation where a mechanical design constraint forces a major change in your software logic mid-project?

Where candidates lose points

Structure your answers

Use the STAR method (Situation, Task, Action, Result) when describing your past projects to ensure clarity and impact.

Highlight your autonomy

Emphasize instances where you took the lead on a project or resolved a critical issue without needing external guidance.

Prepare for the technical deep-dive

Have a high-level overview of your favorite PLC architecture ready to discuss, including why you chose specific programming patterns.

Showcase your industry knowledge

Mentioning specific trends or challenges in the packaging or automation sector can show you are deeply engaged with the industry.

Questions & practice

What is your strategy for communicating technical progress to non-technical stakeholders or clients during an

Medium · Project Management & Problem Solving

What is your strategy for communicating technical progress to non-technical stakeholders or clients during an installation?

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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?

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

  1. Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
  2. Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
  3. Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
  4. Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

  • The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
  • The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

  1. Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
  2. Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
  3. For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
  4. Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

  • The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
  • Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

  1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
  2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
  3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
  4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

  • The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
  • Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

  1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
  2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
  3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
  4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

  • A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
  • The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

What are the key differences you consider when programming for high-speed packaging machines versus general in

Medium · Technical Proficiency & Programming

What are the key differences you consider when programming for high-speed packaging machines versus general industrial plants?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

How do you optimize PLC scan times for complex, multi-axis motion control tasks?

Medium · Technical Proficiency & Programming

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Can you explain your process for integrating HMI/SCADA systems with existing PLC architecture?

Medium · Technical Proficiency & Programming

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

How do you approach debugging a machine that exhibits intermittent faults during the commissioning phase?

Medium · Technical Proficiency & Programming

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

How do you ensure your code remains maintainable and well-documented for future updates or troubleshooting?

Medium · Project Management & Problem Solving

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Describe a challenging installation phase where you had to troubleshoot on-site under tight deadlines.

Medium · Project Management & Problem Solving

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics DRD Recruiting candidates report.

Day 1 — Map the DRD Recruiting loop

  • Write out the reported sequence: Initial Screening, Technical Deep-Dive, Problem-Solving Discussion.
  • 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.

Day 2 — Work PLC programming

  • Spend the session on PLC programming, which DRD Recruiting candidates report being tested on.
  • Write one worked example in PLC programming and time yourself on it.

Deliverable: One timed worked example in PLC programming.

Day 3 — Work Industrial automation

  • Spend the session on Industrial automation, which DRD Recruiting candidates report being tested on.
  • Write one worked example in Industrial automation and time yourself on it.

Deliverable: One timed worked example in Industrial automation.

Day 4 — Work Siemens TIA Portal

  • Spend the session on Siemens TIA Portal, which DRD Recruiting candidates report being tested on.
  • Write one worked example in Siemens TIA Portal and time yourself on it.

Deliverable: One timed worked example in Siemens TIA Portal.

Day 5 — Answer out loud: Technical Proficiency & Programming

  • Answer aloud, timed: Can you describe your experience with Siemens TIA Portal or Schneider platforms in a production environment?
  • Answer aloud, timed: How do you approach debugging a machine that exhibits intermittent faults during the commissioning phase?

Deliverable: Spoken answers to 2 reported Technical Proficiency & Programming question(s), under time.

Day 6 — Answer out loud: Project Management & Problem Solving

  • Answer aloud, timed: How do you handle a situation where a mechanical design constraint forces a major change in your software logic mid-project?
  • Answer aloud, timed: Describe a time you had to manage your own project timeline while also providing support to other team members.

Deliverable: Spoken answers to 2 reported Project Management & Problem Solving question(s), under time.

Day 7 — Dry run for DRD Recruiting

  • Run one full mock under time, then write down the two questions you most want to ask your interviewers.

Deliverable: A completed timed mock and two questions to ask.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Can you describe your experience with Siemens TIA Portal or Schneider platforms in a production environment?

Medium · Technical Proficiency & Programming

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 a mechanical design constraint forces a major change in your software logi

Medium · Project Management & Problem Solving

How do you handle a situation where a mechanical design constraint forces a major change in your software logic mid-project?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 manage your own project timeline while also providing support to other team members

Medium · Project Management & Problem Solving

Describe a time you had to manage your own project timeline while also providing support to other team members.

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?