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IQVIA Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at IQVIA, you are at the intersection of advanced technology and global healthcare. You will contribute to the development of solutions that accelerate clinical research, provide commercial insights, and improve patient outcomes worldwide. This role is critical because your code directly impacts the speed and efficiency with which life-saving medical treatments reach the market. You will work within a complex ecosystem, often dealing with massive datasets and high-stakes regulatory requirements. Whether you are building scalable platforms, implementing software for clinical trials, or working on cloud-based infrastructure, you will be expected to balance technical rigor with the specific needs of the life sciences industry. It is a position for those who thrive in a collaborative environment and are motivated by the mission-driven nature of IQVIA.

This guide is scoped to a Software Engineer candidate at IQVIA.

Focus areas

  • SAS (Statistical Analysis System)
  • SQL (Querying)
  • Data Structures & Algorithms (DSA)

The IQVIA interview process

IQVIA candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Automated Assessments

Candidates complete automated assessments to evaluate their problem-solving abilities.

What to demonstrate

  • Candidates complete automated assessments to evaluate their problem-solving abilities
  • Depth in SAS (Statistical Analysis System)

How to prepare

  • Answer aloud and timed: Can you explain the difference between SQL and NoSQL databases in the context of large-scale data?
  • Answer aloud and timed: How do you handle CTEs (Common Table Expressions) and complex joins in SQL?

Technical Deep Dives

In-depth technical interviews focusing on specific skills and knowledge relevant to the role.

What to demonstrate

  • In-depth technical interviews focusing on specific skills and knowledge relevant to the role
  • Depth in SAS (Statistical Analysis System)

How to prepare

  • Answer aloud and timed: What is your experience with.NET architecture and how do you approach SOLID principles in your code?
  • Answer aloud and timed: Can you describe how you would design a system to handle high-concurrency requests?

Behavioral Discussions

Conversations aimed at assessing cultural fit and teamwork potential.

What to demonstrate

  • Conversations aimed at assessing cultural fit and teamwork potential
  • Depth in SAS (Statistical Analysis System)

How to prepare

  • Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
  • Re-read the description of the behavioral discussions above and write down what you would ask to confirm before it.

Final Negotiation

Discussion regarding salary and background with HR or talent partners.

What to demonstrate

  • Discussion regarding salary and background with HR or talent partners
  • Depth in SAS (Statistical Analysis System)

How to prepare

  • Answer aloud and timed: How do you optimize a string manipulation function for performance?
  • Answer aloud and timed: Can you write a function to solve a specific business logic problem while considering edge cases?

Where candidates lose points

Own your resume

Every project you list is fair game. Be prepared to explain the "why" behind every technical decision you made in those projects.

Master the basics

Do not neglect core computer science subjects like OOPS, SOLID principles, and database normalization. These are frequently tested.

Showcase industry awareness

If you have experience in life sciences or healthcare, highlight it. If not, demonstrate that you have researched the industry and understand the criticality of data integrity.

Prepare for ambiguity

Some interviewers may use scenario-based questions to see how you handle incomplete information. Don't panic; ask clarifying questions to define the scope before you start solving.

Questions & practice

Can you walk me through your approach to solving a medium-to-high difficulty DSA (Data Structures and Algorith

Medium · Coding and Algorithms

Can you walk me through your approach to solving a medium-to-high difficulty DSA (Data Structures and Algorithms) problem?

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?

How do you optimize a string manipulation function for performance?

Medium · Coding and Algorithms

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?

Can you write a function to solve a specific business logic problem while considering edge cases?

Medium · Coding and Algorithms

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?

Can you explain the difference between SQL and NoSQL databases in the context of large-scale data?

Medium · Technical and Domain Knowledge

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

  1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
  2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
  3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
  4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

  • A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
  • How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Can you describe how you would design a system to handle high-concurrency requests?

Medium · Technical and Domain Knowledge

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?

What are the nuances of using SAS and R for statistical output and data analysis?

Medium · Technical and Domain Knowledge

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?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

  1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
  2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
  3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
  4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

  • Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
  • How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics IQVIA candidates report.

Day 1 — Map the IQVIA loop

  • Write out the reported sequence: Automated Assessments, Technical Deep Dives, Behavioral Discussions, Final Negotiation.
  • 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 4 reported rounds, with the weakest marked.

Day 2 — Work SAS (Statistical Analysis System)

  • Spend the session on SAS (Statistical Analysis System), which IQVIA candidates report being tested on.
  • Write one worked example in SAS (Statistical Analysis System) and time yourself on it.

Deliverable: One timed worked example in SAS (Statistical Analysis System).

Day 3 — Work SQL (Querying)

  • Spend the session on SQL (Querying), which IQVIA candidates report being tested on.
  • Write one worked example in SQL (Querying) and time yourself on it.

Deliverable: One timed worked example in SQL (Querying).

Day 4 — Work Data Structures & Algorithms (DSA)

  • Spend the session on Data Structures & Algorithms (DSA), which IQVIA candidates report being tested on.
  • Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 5 — Answer out loud: Technical and Domain Knowledge

  • Answer aloud, timed: Can you explain the difference between SQL and NoSQL databases in the context of large-scale data?
  • Answer aloud, timed: How do you handle CTEs (Common Table Expressions) and complex joins in SQL?

Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.

Day 6 — Answer out loud: Coding and Algorithms

  • Answer aloud, timed: Can you walk me through your approach to solving a medium-to-high difficulty DSA (Data Structures and Algorithms) problem?
  • Answer aloud, timed: How do you optimize a string manipulation function for performance?

Deliverable: Spoken answers to 2 reported Coding and Algorithms question(s), under time.

Day 7 — Answer out loud: Project-Based and Behavioral

  • Answer aloud, timed: Can you describe a challenging project you worked on and the specific role you played in its success?
  • Answer aloud, timed: How do you handle situations where you have to meet tight deadlines while working on multiple projects?

Deliverable: Spoken answers to 2 reported Project-Based and Behavioral question(s), under time.

Behavioural preparation

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

How do you handle CTEs (Common Table Expressions) and complex joins in SQL?

Medium · Technical and Domain Knowledge

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?

What is your experience with.NET architecture and how do you approach SOLID principles in your code?

Medium · Technical and Domain Knowledge

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?

Can you describe a challenging project you worked on and the specific role you played in its success?

Medium · Project-Based and Behavioral

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 situations where you have to meet tight deadlines while working on multiple projects?

Medium · Project-Based and Behavioral

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?

Tell me about a time you had to explain a complex technical issue to a non-technical stakeholder.

Medium · Project-Based and Behavioral

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 approach feedback, both when giving it to peers and receiving it from leadership?

Medium · Project-Based and Behavioral

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?