By Vadym Lobariev, founder of MindHunt — recruiting technical specialists across Europe and Ukraine since 2011
Sourcing is the part of recruiting that most companies do poorly — not because it's complicated, but because it requires a different mindset than the rest of the hiring process.
Most hiring processes are reactive. A role opens, a job description goes up, applications come in. Sourcing is the opposite: proactive, outbound, going to find the people you need rather than waiting to see who finds you.
For technical roles, this distinction is critical. The engineers who will have the most impact on your product are almost never browsing job boards. They are employed, working on things they find interesting, and not looking for anything. Sourcing is how you find them anyway.
What Sourcing Actually Is
Sourcing is the process of identifying potential candidates who are not actively applying for your role — and bringing them into your pipeline.
It is not the same as posting a job. It is not the same as reviewing inbound applications. It is the active work of going out, finding people who match what you need, and opening a conversation.
For technical roles in 2026, sourcing typically means:
- Searching LinkedIn for profiles that match your requirements
- Reviewing GitHub for developers whose actual code and contributions signal the right skills
- Using platforms specific to your target market (DOU and Djinni for Ukraine and Eastern Europe)
- Running personalised outreach to the people you've identified
The goal of sourcing is not to fill a role in one search. It is to build a pipeline of qualified, engaged candidates from which you can make good hires consistently.
The Sourcing Toolkit in 2026
LinkedIn remains the primary sourcing platform for professional technical talent. Most engineers have a profile. Many are reachable there even when they're not actively looking.
The challenge with LinkedIn sourcing is finding the right people efficiently. The traditional approach — Boolean search — gives you control over search logic but requires expertise to use well and time to execute. A well-constructed Boolean string for a Senior DevOps Engineer with Kubernetes experience and a fintech background might look like:
("DevOps engineer" OR "SRE" OR "platform engineer") AND
(Kubernetes OR "k8s") AND (fintech OR "financial services")
NOT (junior OR intern)This works. But it requires knowing how to build it, iterating when results are too broad or too narrow, and running it manually across multiple searches.
AI-powered sourcing tools like MindHunt AI replace this with a simpler approach: you paste in a job description and the tool searches LinkedIn automatically, scoring candidates by relevance. No Boolean expertise required. The job description becomes the search query.
GitHub
For technical roles — particularly backend engineering, DevOps, and data engineering — GitHub is one of the most honest sourcing signals available.
A developer's public repositories tell you what they actually build. Their contribution history shows engagement and consistency. The quality of their code, documentation, and project structure reveals technical standards that a CV cannot.
Sourcing on GitHub directly is complex — the platform is not designed for recruiter search. The practical approach: use GitHub to qualify and assess candidates you've already found on LinkedIn, or use a tool that searches both simultaneously. MindHunt AI allows you to source across LinkedIn and GitHub from the same interface — and if you identify a strong GitHub profile, you can find the corresponding LinkedIn profile without leaving the platform.
DOU and Djinni for Ukraine and Eastern Europe
These two platforms are the most important sourcing channels that most Western hiring guides don't mention — and they are essential if you're hiring from the Ukrainian or Eastern European market.
DOU is the largest Ukrainian developer community. It functions as a job board, professional forum, and industry publication. Most Ukrainian developers of any seniority have a DOU presence. The platform publishes reliable salary data, technology usage statistics, and hiring trends that are far more accurate for this market than any Western source.
Djinni is purpose-built for matching Ukrainian and Eastern European developers with companies. The model is candidate-driven: developers set their preferences — salary expectations, technologies, remote or on-site, company type — and companies reach out to candidates who match. Response rates are significantly better than cold LinkedIn outreach because candidates on Djinni have already indicated openness to opportunities.
For any company seriously considering Ukrainian technical talent, these two platforms are not optional additions to the sourcing toolkit — they are the primary channels.
Boolean Search: Still Useful, Being Displaced
Boolean search is worth understanding even in 2026, because it gives you precise control over search logic when you need it.
The core operators:
- AND — both terms must be present:
Python AND machine learning - OR — either term:
"backend engineer" OR "software engineer" - NOT or - — exclude a term:
developer NOT junior - Quotes — exact phrase:
"full stack developer" - Parentheses — group logic:
(React OR Vue OR Angular) AND senior
Where Boolean still wins: highly specific technical searches where you know exactly what you're looking for and need to narrow a large pool. Where AI sourcing wins: broader searches, unfamiliar roles, or situations where you don't know the exact terminology candidates use in their profiles.
The two approaches are complementary. Experienced sourcers use both.
Outreach: The Part That Determines Whether Sourcing Works
Finding the right candidates is half the job. Getting them to respond is the other half — and this is where most sourcing efforts fail.
Senior technical professionals receive a high volume of recruitment messages. Most of them are templated, generic, and immediately recognisable as mass outreach. Most of them get ignored.
The messages that get responses have three things in common:
They are specific. They reference something real about the candidate — a technology they use, a project they've worked on, a company they've been at. Generic "I came across your profile and was impressed" language is the signal that the recruiter didn't actually look.
They make a clear case for why this role is relevant to this person. Not a list of job requirements — a reason why someone with their background would find this particular opportunity interesting.
They are short. Three to four sentences. The goal of the first message is to start a conversation, not to close a deal. Long first messages get read less.
AI personalisation tools can help at scale — MindHunt AI generates personalised outreach sequences for each candidate based on their profile. But the quality of the personalisation still depends on the quality of the brief you started with. Garbage in, garbage out applies to AI outreach as much as anything else.
Contact Details: The Last Sourcing Bottleneck
You've found the right person on LinkedIn. You've written a good message. Now you need to reach them.
LinkedIn InMail response rates have declined as the volume of outreach on the platform has increased. Many experienced engineers check messages infrequently or filter them aggressively.
Email and — in some markets, particularly Ukraine — Telegram and phone are often more effective channels for initial outreach. The challenge is finding those contact details.
Manually tracking down email addresses from GitHub profiles, personal websites, or other sources is time-consuming. MindHunt AI addresses this directly: once you identify a candidate, email and phone numbers are fetched in one click from the same interface. This removes a sourcing step that used to cost significant time for each candidate.
Building a Sourcing Pipeline, Not Just Filling Roles
The most effective technical sourcing is not reactive — finding people when a role opens. It is building a warm pipeline of candidates before you need them.
This means: keeping contact with strong candidates who weren't the right fit for a previous role. Staying connected with people who declined because the timing wasn't right. Following engineers whose work you respect before you have a role for them.
When the role opens that matches someone in your pipeline, the time-to-hire compresses significantly. The alternative — starting a cold search every time — means every hire takes the same amount of time, regardless of how long you've been active in the market.
When to Work With a Sourcing Specialist
Sourcing is skilled work. Doing it well requires platform knowledge, outreach craft, the ability to assess technical profiles, and the consistency to maintain a pipeline over time.
If your team doesn't have dedicated sourcing capacity, or if you're hiring in a market you don't know well — Ukraine, Eastern Europe, specific technical specialisations — working with a specialist recruitment agency or using a dedicated AI sourcing tool will produce better results than adding sourcing to an already full hiring manager's responsibilities.
If you'd like to discuss a specific search — what you're looking for, where you're looking, and what a realistic sourcing strategy looks like — book a call with MindHunt. We'll tell you honestly what to expect.
Related reading: Technical Recruitment: A Practical Process Guide · Top Sites to Hire Programmers in 2026 · How to Hire Developers in Ukraine
Written by
Vadym Lobariev
MindHunt is an AI powered recruitment firm for founders, C-level and hiring managers who are tired of posting and praying. We execute a proven sourcing process for your hardest roles and show you the work every week — so you can make hires with confidence, not hope.
