Phastar · Software Engineer
Updated · 2026-10-02

Phastar Software Engineer
Interview Guide

THE 60-SECOND BRIEF

As a Software Engineer (often designated as Programmer or Senior Programmer within the organization), you are a critical architect of the data-driven solutions that power Phastar’s clinical research and biometrics services. Your work directly impacts the integrity and efficiency of clinical trials, transforming complex research data into actionable insights that contribute to life-saving medical advancements. This role is unique because it sits at the intersection of sophisticated software engineering and specialized clinical data standards. Whether you are working in Oncology, Submission, or general SAS/R programming, you are responsible for building robust, scalable, and compliant systems. You will collaborate with cross-functional teams, including statisticians and data managers, to solve high-stakes technical problems in a highly regulated environment.

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

Phastar candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Software EngineeringProgramming Language Proficiency (General)Problem Solving

17 min read

Browse Software Engineer questions

See the practice prompts

11Practice promptsAcross five skill areas

As a Software Engineer (often designated as Programmer or Senior Programmer within the organization), you are a critical architect of the data-driven solutions that power Phastar’s clinical research and biometrics services. Your work directly impacts the integrity and efficiency of clinical trials, transforming complex research data into actionable insights that contribute to life-saving medical advancements. This role is unique because it sits at the intersection of sophisticated software engineering and specialized clinical data standards. Whether you are working in Oncology, Submission, or general SAS/R programming, you are responsible for building robust, scalable, and compliant systems. You will collaborate with cross-functional teams, including statisticians and data managers, to solve high-stakes technical problems in a highly regulated environment.

01

Screening Call

reported

Initial call to discuss your background and interest in the company.

What to demonstrate

  • Initial call to discuss your background and interest in the company
  • Depth in Software Engineering

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.
Phastar Software Engineer candidate reports ↗
02

Technical Assessments

reported

In-depth technical assessments or interviews with senior team members.

What to demonstrate

  • In-depth technical assessments or interviews with senior team members
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: Describe a time you had to troubleshoot a complex data issue during a submission process.
  • Answer aloud and timed: What strategies do you use to ensure your code is reproducible and compliant with industry standards?
Phastar Software Engineer candidate reports ↗

PracHub editorial advice for the preparation topics above.

01

Articulate your process

When solving problems, think out loud. Interviewers want to see how you approach ambiguity and how you structure your logic.

02

Focus on impact

When discussing past projects, clearly state the challenge, the action you took, and the measurable impact on the project or the patient data.

03

Prepare for domain questions

Ensure you can explain why certain programming choices are made in a regulated environment versus a general software setting.

Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.

8 technical prompts0 include a worked solution

Merge partitioned event streams into one ordered feed with bounded lateness

hard
k-way mergewatermarksout-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach
  1. Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
  2. Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
  3. Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
  4. Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.
Follow-up
  • The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
  • The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

Track a rolling failure rate per destination for circuit decisions

easy
sliding windowring buffercircuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach
  1. Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
  2. Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
  3. State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
  4. Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
  • The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
  • A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Find overlapping job attempts and peak concurrency from lease records

medium
sweep lineintervalsleases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach
  1. Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
  2. For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
  3. For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
  4. Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
  • A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
  • Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

Built from the rounds and topics Phastar candidates report.

Small steps. Visible outcomes.0 / 7 completed
ONE WEEK · YOUR PACE

Prepare, practise & reflect

One practical outcome each day. Spend longer where you need it.

0 / 7 done
01Map the Phastar loop
  • Write out the reported sequence: Screening Call, Technical Assessments.
  • 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 2 reported rounds, with the weakest marked.

02Work Software Engineering
  • Spend the session on Software Engineering, which Phastar candidates report being tested on.
  • Write one worked example in Software Engineering and time yourself on it.

Deliverable: One timed worked example in Software Engineering.

03Work Programming Language Proficiency (General)
  • Spend the session on Programming Language Proficiency (General), which Phastar candidates report being tested on.
  • Write one worked example in Programming Language Proficiency (General) and time yourself on it.

Deliverable: One timed worked example in Programming Language Proficiency (General).

04Work Problem Solving
  • Spend the session on Problem Solving, which Phastar candidates report being tested on.
  • Write one worked example in Problem Solving and time yourself on it.

Deliverable: One timed worked example in Problem Solving.

05Answer out loud: Technical and Domain Proficiency
  • Answer aloud, timed: How do you optimize your code for performance when handling large datasets?
  • Answer aloud, timed: Can you explain your experience with SAS versus R in a clinical context?

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

06Rehearse your own examples
  • Prepare three examples from your own work where you made the decision, each with the outcome you can quantify.

Deliverable: Three examples written out, each with a number attached.

07Dry run for Phastar
  • 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.

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.

Can you explain your experience with SAS versus R in a clinical context?

medium
Technical and Domain Proficiency

Can you explain your experience with SAS versus R in a clinical context?

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 troubleshoot a complex data issue during a submission process.

medium
Technical and Domain Proficiency

Describe a time you had to troubleshoot a complex data issue during a submission process.

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 errors or data inconsistencies within a clinical trial pipeline?

medium
Technical and Domain Proficiency

How do you handle errors or data inconsistencies within a clinical trial pipeline?

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?
  • 01

    Can you explain your experience with SAS versus R in a clinical context?

  • 02

    Describe a time you had to troubleshoot a complex data issue during a submission process.

  • 03

    How do you handle errors or data inconsistencies within a clinical trial pipeline?

PracHub preparation framework ↗
How difficult are the technical assessments?

The assessments are practical and focused on your day-to-day work. If you are comfortable with your core programming language and understand clinical data workflows, you will find them to be a fair reflection of the job.

Phastar Software Engineer candidate reports ↗
What is the company culture like at Phastar?

Phastar values technical excellence, collaboration, and a commitment to quality. You will find an environment that encourages continuous learning and professional growth within the clinical research field.

Phastar Software Engineer candidate reports ↗
How long does the process take?

The timeline can vary, but Phastar aims for a transparent and efficient process. Expect a few weeks from the initial screen to a final offer, depending on team availability.

Phastar Software Engineer candidate reports ↗
What topics does Phastar test in interviews?

Phastar interviews most often cover Version Control (Git), Software Engineering, Statistical Programming, Programming Language Proficiency (General), and Statistical Modeling. The exact emphasis depends on the specific role you apply for.

Phastar 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.