Rapid Scaling for a European E‑commerce Product

+15 engineers requested in ≤12 weeks, across 9 parallel roles and a multi-team interview structure

Context & Key Conditions

The client is a European e‑commerce product with an aggressive roadmap: expanding the product catalog, integrating with payment/tax/logistics providers, improving front-end performance, and preparing for seasonal peak loads (e.g., Black Friday).

Polytech already had an integrated team of ~35 specialists on the account and received a request to add +15 engineers within ≤12 weeks.

Roles / stack: Senior & Middle Full-Stack .NET (React + .NET, Azure), Senior & Middle Back-End .NET, QA Automation, Manual QA, Performance Engineering

Engagement model: Fully remote (any location), contracted via Polytech under Ukrainian FOP model

Candidate requirements: E‑commerce domain experience, API & web services design/development, English for product and management communication

The Challenge

We needed to scale the team without impacting delivery, while dealing with:

A multi-team client structure, meaning different interview streams and stakeholders
9 parallel roles with different expectations and leveling criteria
Live interviews only (no take-home assignments), with decisions made in tight scheduling windows
Final offer approvals on the client side, creating a high risk of candidate drop-off due to delays

Our Approach

We designed "hiring as a product": standardizing technical criteria and scorecards across all streams, running parallel pipelines under a single priority model, strengthening pre-screening on Polytech's side, and introducing rigorous operational discipline (SLAs, dashboards, WBR cadence).

Interview Framework (Single Consistent Backbone):

Stages: Polytech screening → Polytech technical interview → Client technical interview → Client manager interview
Principles: Live-only, fast decisions, transparent communication of expectations and next steps
Risk management: Backup interview slots and asynchronous fallback options where feasible

Delivery Speed

starts within target window

Engineers onboarded and productive

≤12

weeks timeline

From kickoff to full team expansion

parallel roles

Across multiple teams simultaneously

What We Delivered

Standardized operating model across 9 roles: competency matrices (QA / Back-End / Full-Stack / Performance), soft-skill expectations, and English proficiency requirements
Parallel pipelines with centralized prioritization to avoid slot contention between teams
Stronger Polytech technical screening: short technical quizzes + deep live sessions to reduce noise and increase signal
Role/level routing: if a candidate didn't fit the initial role, we proactively proposed a relevant alternative role/level
Cost-efficient sourcing: LinkedIn, referrals, EU/UA job boards; targeted use of Djinni for peak waves
Transparency via ATS and dashboards: Zoho Recruit and an SLA for feedback in 2–72 hours
Process cadence: daily reviews during the first three weeks, then a weekly business review (WBR) focused on conversions, bottlenecks, escalations, and cross-team priority balancing
Sell calls with Tech Leads/CTO: clear explanation of stack, role impact, and roadmap to reduce drop-off

Results

We delivered 13 starts within ≤12 weeks by issuing 17 offers, with 13 candidates starting within the target window (two additional offers landed on the edge/outside the window due to notice periods and vacations).

Funnel & Metrics (Multi-stream hiring across 9 roles):

Candidates processed: ~350 (excluding the initial screening outflow)
Polytech technical interviews: 80 (18 QA, 29 Full-Stack, 26 Back-End .NET, 7 Performance)
Submitted to the client for final stages: 38 candidates
Speed-to-first-CV: first relevant profiles delivered within the first days after kickoff

Release cadence and defect rate did not degrade — delivery remained within existing SLAs. The client specifically highlighted the quality of Polytech's pre-screening, the transparency of the process, and the risk-managed execution of a high-velocity multi-stream hiring model.

Quality Outcomes

~92%

90-day retention

12 out of 13 continued past 90 days

~350

candidates processed

Across all hiring streams

days to first PR

Median ramp-up time

Risk Management

Key Risks and How We Mitigated Them:

Vacations / overloaded interview slots (Aug–Oct): tight slot booking, reserve windows, priority synchronization
High competition for .NET talent in the EU: early sell calls, clear compensation bands, structured offer scenarios
Infrastructure instability (connectivity/power): backup slots, fast rescheduling without "silent" periods
Delays in client approvals: weekly escalations and regular touch-points with candidates to keep engagement high

Client Outcome:

Rapid scaling without delivery degradation during a peak-ready period (Black Friday Peak)
A controlled multi-stream hiring engine: 9 roles, multiple teams — one transparent process
Higher pipeline quality due to strong pre-screening and signal-focused evaluation
A repeatable model for future hiring waves: playbooks, metrics, and dashboards