Most data jobs are filled before they ever appear on a job board. This lesson teaches you how to use informational interviews as a systematic strategy for building relationships with data professionals, surfacing hidden hiring signals, and converting warm conversations into referrals — before the competition even knows the role exists.
Imagine two candidates both applying for the same data analyst role at a mid-sized fintech company. The first candidate spotted the listing on LinkedIn, submitted a polished resume through the portal, and waited. The second candidate had coffee with a data engineer at that same company six weeks earlier, learned that the team was planning to hire once a new product line launched, followed up at exactly the right moment, and was referred directly to the hiring manager before the job was ever posted publicly. Both candidates are equally qualified. One of them gets the interview the same week they ask for it. The other sits in an ATS queue with 340 other applicants.
This is the hidden job market in action — and it's not a myth. Research from LinkedIn and various labor economists consistently shows that somewhere between 30% and 70% of job offers are filled before a formal posting ever goes public. The exact number varies by industry and seniority level, but the principle holds: companies prefer to hire people they already know or have been vouched for. Informational interviews are how you get into that pre-posting network before you need a job, which is exactly when you have the most leverage.
By the end of this lesson, you'll understand not just how to ask for an informational interview, but how to use it as an intelligence-gathering and relationship-building tool that creates genuine hiring opportunities. You'll know how to identify the right people to talk to, what to ask them, how to follow up without being annoying, and how to convert those conversations into referrals when the timing is right.
What you'll learn:
This lesson assumes you're already familiar with the basics of the data job search — you understand different role types, you've at least started building out your portfolio, and you have a LinkedIn profile that's reasonably complete. If you haven't done that groundwork yet, take a look at our lesson on networking for data professionals and make sure your profile is in shape with how to create a data analyst LinkedIn profile that attracts recruiters. This lesson is about what you do after your foundation is set — turning passive visibility into active conversations.
Before we talk tactics, let's talk about why the hidden job market exists at all. Understanding the mechanics helps you use informational interviews more strategically.
When a hiring manager needs to add someone to their data team, their first instinct isn't to write a job description and post it to ten job boards. That process is slow, expensive (both in recruiter time and in job board fees), and produces noisy results. A single data analyst posting at a tech company can generate hundreds of applications, most of which are nowhere close to qualified. For a busy data team, the interview and filtering process alone can consume weeks of productive time.
So hiring managers do what all rational people do: they take the path of least resistance. They ask their current team if they know anyone. They ping a former colleague who just left the company. They think back to that sharp candidate they passed on six months ago because there wasn't a role at the time. They reach out to someone who gave a good talk at a local Meetup. Only when those informal channels come up empty does the formal posting go live — and often, even then, the internal referral network stays active and privileged throughout the process.
Data roles, in particular, operate this way for a specific reason: technical hiring is hard. Hiring managers have been burned by candidates who looked great on paper but couldn't write a clean SQL query under pressure, or who understood machine learning theory but had never worked with messy, real-world data. When someone they already trust says "I know someone who's good," that's worth more than a resume full of keywords. The technical interview process for data roles is rigorous precisely because the stakes of a bad hire are high — which makes the pre-vetting that a referral provides genuinely valuable to a hiring manager.
Key insight
You're not trying to game the system. You're trying to participate in the system as it actually works, rather than the idealized version where the best-qualified candidate always surfaces through a job board.
The biggest mistake job seekers make with informational interviews is being random about who they contact. They shoot messages to every data professional they can find, hoping someone bites. The result is a bunch of generic conversations that don't go anywhere.
The better approach is deliberate targeting. You want to identify people who are:
At the right seniority level — Individual contributors (data analysts, data engineers, data scientists) are often your best sources of honest, ground-level intelligence. They know what the team actually does day-to-day, what the manager is like, and whether the team is growing. Directors and VPs are valuable too, but they're harder to reach and often don't have granular hiring timeline knowledge.
At companies you genuinely want to work for — Don't spread yourself thin across 50 companies. Pick 10–15 target companies based on industry fit, the kind of data problems they work on, and culture signals. If you haven't worked through this filtering process yet, the lesson on decoding the data job description has a useful framework for evaluating company fit before you even talk to anyone.
In roles adjacent to or slightly above where you're aiming — A data analyst who's been at the company two years knows more about the realistic hiring environment than a recruiter does.
Connected to you through some genuine thread — Shared alma mater, a mutual connection, a conference they spoke at, an article they published, a GitHub project you've actually looked at. The thinner the connection, the harder the outreach.
Here's a practical workflow for building this list:
Step 1: Start with LinkedIn's "People" filter. Search for your target company, then filter by "Data" in the job title field. Look at everyone from analyst to director level. Note who has been there 1–3 years (long enough to know the culture, not so long they've lost perspective on what the job market looks like).
Step 2: Cross-reference with GitHub, Kaggle, and conference talk archives. If someone's published a dataset, given a Meetup talk, or contributed to an open-source tool, that's a conversation starter that's grounded in something real.
Step 3: Check if anyone from your existing network works at or has worked at the company. Former employees are gold — they can speak candidly and often still have active relationships inside the organization.
Step 4: Build a simple tracking spreadsheet. Use columns for Name, Company, Role, Connection Path, Outreach Date, Response, Conversation Date, and Follow-Up Notes. You're running a small CRM here. Even ten contacts gets messy without it.
Tip
Prioritize people who've posted publicly about their work — they've already demonstrated a willingness to share knowledge. Someone who's published a data blog or presented at dbt Community isn't going to be annoyed that you reached out about their field.
This is where most people undermine themselves. They write messages that are too long, too vague, too obviously job-hunting, or too obviously flattering in a hollow way. Let's look at what works and why.
The core principle: you're not asking for a job, you're asking for 20 minutes of their experience. That's a fundamentally different ask, and it needs to feel that way — not as a polite fiction, but because it's genuinely true. You're not there to pitch yourself. You're there to learn. The job opportunities that emerge from that conversation are a byproduct of the relationship, not the goal of the meeting itself.
Warning
If you go into an informational interview with the primary goal of getting a referral, the other person will feel it immediately. People are remarkably good at detecting when a "conversation" is actually a sales call with extra steps. This poisons the well before you even get started.
Here's a LinkedIn connection request message that actually gets replies:
Hi Maya,
I came across your talk on revenue attribution modeling at the
Chicago Analytics Summit — the part about reconciling CRM and
warehouse data against marketing touchpoints was exactly the
problem I've been wrestling with in a project I'm working on.
I'm transitioning into a data analyst role from a finance
background and would love to ask you a few questions about
how you made the shift and what your team looks for in new
hires. Would you be open to a 20-minute call sometime in
the next few weeks?
No worries if you're swamped — I know your time is limited.
— Jordan
Notice a few things here. The opening line shows genuine familiarity with their specific work — not "I saw your profile and was impressed" but "I watched a specific thing you made and it connected to something I'm actively doing." The ask is bounded (20 minutes, not "a quick chat" which implies no limit). And the closing explicitly releases them from obligation, which paradoxically makes people more likely to say yes.
For cold email, the same structure applies with a slightly longer intro:
Subject: Quick question from someone following your work on
retail analytics
Hi Marcus,
I found your case study on customer churn prediction at
[Company] through the Towards Data Science article you wrote
last spring. The section on handling class imbalance with
SMOTE in a production pipeline was genuinely useful for a
personal project I've been working on.
I'm currently transitioning from operations analytics into
a dedicated data role and am trying to understand what
separates strong analyst candidates from the rest in
e-commerce environments. You've been doing this work for
a few years at a company I'd love to understand better.
Would you be willing to spend 20 minutes on a call? I'll
come prepared with specific questions so we use your time
efficiently. Completely understand if you're not available.
Best,
Jordan Lee
For a detailed guide on structuring cold outreach messages for data professionals — including timing strategies and follow-up cadence — see how to write a cold outreach message to data professionals that actually gets a response.
Note
Response rates vary significantly. Expect somewhere between 20–40% on well-crafted, targeted messages with a real connection thread. Don't interpret silence as rejection — follow up once after one week, then move on. Some people will respond months later when their schedule opens up.
You've got the meeting. Now what? This is where most people blow it — they either free-form ramble through 45 minutes of vague conversation, or they pepper the person with questions about open positions so directly that it becomes uncomfortable.
The better approach is to use the conversation as an intelligence-gathering session that's genuinely useful to both of you. You're learning about their world; they're getting the satisfaction of sharing expertise with someone who actually wants to learn it. That's a fair exchange.
Structure the 20 minutes roughly like this:
Minutes 1–3: Brief context setting. One minute on who you are and what you're working on (a portfolio project, a certification, a career transition). Keep this tight. You're not here to talk about yourself.
Minutes 3–15: The core questions. Pick 5–7 from the list below, calibrated to what you actually want to learn from this specific person. Don't ask all of them.
Minutes 15–18: The hiring-signal questions. This is where you listen carefully.
Minutes 18–20: Close graciously and open the door to the relationship continuing.
Here are questions organized by what they're actually designed to surface:
Questions that reveal team structure and growth:
Questions that reveal real skill requirements:
Questions that reveal hiring intentions (the high-signal ones):
That last cluster is where you start to hear things like: "Actually, we've been talking about adding another analyst — we just kicked off a partnership with a logistics company and we're going to need someone who can handle the integration work." That's a job that doesn't exist yet on any job board. But you just heard about it.
Key insight
The best informational interviews feel like two curious people talking about a field they both care about. When that's genuinely happening, the hiring signal questions land naturally because you've built enough rapport that the other person wants to be helpful.
Questions for the close:
The first closing question is important: getting referred to another person by your contact is one of the most powerful warmth signals in networking. When you reach out to that third person and say "Marcus suggested I talk to you," your open rate and response quality jump dramatically.
You can read the transcript of an informational interview and miss most of what actually happened. Real hiring intelligence comes from reading between the lines of what people say — and knowing what follow-up questions to ask when you hear something interesting.
Here are specific signal phrases and what they actually mean:
| What they say | What it might mean |
|---|---|
| "We've been really slammed lately" | The team is understaffed and may be building a case for a hire |
| "We're in the middle of a big migration" | Technical projects often trigger headcount justifications |
| "My manager has been talking about restructuring the team" | Change is coming — change creates openings |
| "We just raised a Series B" | Growth mode; hiring typically accelerates within 3–6 months |
| "Honestly, we could use someone who knows X" | They just told you exactly what gap to fill |
| "I've been doing double duty since [colleague] left" | There may be a backfill happening slowly |
When you hear any of these signals, your job is to follow up in the conversation with genuine curiosity, not with "So is that a role I could apply for?" The right move is: "That sounds like a lot — how is the team managing that workload?" or "Is that migration something the team is doing internally, or are you bringing in help?" You're probing naturally, not pivoting to a pitch.
Tip
Take notes during the call, but don't be so focused on note-taking that you stop listening. The best practice is to jot a few keywords and then write full notes within 30 minutes of the call ending while it's still fresh.
The conversation itself is only the beginning. The real value of informational interviewing comes from maintaining the relationship over time — and this is where most people fail. They have a great conversation, send a thank-you note, and then never follow up until they desperately need something. That's not a relationship; that's a transaction with a longer tail.
Here's a follow-up structure that works:
Within 24 hours: Send a thank-you message that's specific. Not "Thanks so much for your time!" but something like: "Thanks for the conversation — your point about the difference between a data team that's consultative versus one that's embedded in product made me think differently about how I'm evaluating companies. I'm going to look into the dbt resources you mentioned."
Specific thank-you notes accomplish two things: they show you were actually listening, and they give the other person a snapshot of what you took away.
At 2–4 weeks: Share something genuinely relevant. Did you read an article about something they mentioned? Did you work through a project that connects to your conversation? Did you attend the Meetup they recommended? Send a brief, low-pressure message: "Hey Maya — I ended up reading the Fishtown Analytics blog post you mentioned about semantic layers. You were right, it clicked a lot better once I worked through the tutorial. Hope things are going well on your end."
No ask. Just staying in the conversation.
At 2–3 months: A slightly more substantive check-in. You might share a portfolio project that connects to something you discussed, or comment on something they've posted publicly. This is also a natural moment to mention — casually, not desperately — that your search is progressing: "I've been doing a few interviews at smaller companies, still looking for the right fit. If you happen to hear of anything in analytics at your company or anywhere else in your network, I'd appreciate a heads-up."
This is not begging. This is telling someone clearly what you're looking for so they can help you if an opportunity comes up. People can't make referrals for people whose situation they don't know.
When something relevant happens at their company: You see a job posting go live, or you notice on LinkedIn that their team hired two people. Send a brief message acknowledging it: "I noticed [Company] just posted a senior analyst role — looks like the team is growing. Hope that means things are going well!" This is low-pressure, topical, and keeps you visible without being pushy.
Warning
Do not follow up more than once between contacts on your end unless they respond and open a thread. Aggressive follow-up turns a potential ally into someone who now actively avoids you.
Here's the moment everyone's actually waiting for: a job opens up at a company where you have a contact, and you want to be referred. How do you make this ask without blowing up the relationship you've built?
First, timing matters more than you think. The best moment to ask for a referral is as soon as you see the posting go live or hear that something is opening up — before the role fills informally, before they've screened 50 applications, before the hiring manager has already committed to a candidate. This is why staying warm matters: you want to be the person who hears about it early.
The ask itself should be direct but low-pressure:
Hi Maya,
I noticed that [Company] just posted a data analyst role
on the growth team — this looks exactly like the kind of
work we talked about back in March.
I'd love to formally apply. Would you be comfortable
putting in a referral or letting the hiring manager know
I'm applying? Completely understand if it's not the right
fit from your end, but given what we've discussed, I
thought it was worth asking.
Either way, I'm going to apply through the portal — I
just wanted you to hear it from me first.
Thanks,
Jordan
Notice: you're applying regardless. You're not asking them to get you the job. You're asking them to put their name behind your application. That's a much smaller ask, and the fact that you've built a genuine relationship over the past few months means they have something concrete to say: "I talked to Jordan for 20 minutes earlier this year and they were sharp — they already understood the revenue attribution problem we've been dealing with."
That's worth a lot more than a random employee referral where someone submits your resume just to get the referral bonus.
Key insight
A referral from someone who genuinely knows your work and thinking is worth ten times a referral from someone who just knows your name. All the time you've invested in maintaining the relationship is why your referral carries weight.
One informational interview is a nice conversation. A dozen of them over six months, systematically maintained, is a job search strategy. Here's how to build and manage it at scale.
Your tracking spreadsheet should have at minimum these columns:
Name | Company | Role | How We Connected | Date Contacted
| Response (Y/N) | Call Date | Key Notes | Follow-up 1
| Follow-up 2 | Status
Status should be one of: Cold, Contacted, Responded, Call Scheduled, Call Complete, Warm, Hot, or Converted. You're running a pipeline.
Set a weekly rhythm. Every Monday, review your tracker and identify:
Aim for 2–3 new outreach messages per week and 2–3 ongoing relationship touches. At that pace, within three months you'll have a network of 20–30 warm contacts who know you're looking and what you're looking for. That's meaningful coverage.
Triage your conversations by signal strength. Not every informational interview will be equally promising. After each call, note your signal read:
High-signal contacts get more frequent, more intentional follow-up. Low-signal contacts get one or two gentle touches and then you maintain a quarterly pulse.
Tip
Don't ghost low-signal contacts entirely. The data industry is smaller than it seems. Someone who has no opening today might move to a company that's actively hiring six months from now, or they might know exactly the right person to introduce you to. A quarterly "hope things are going well" is easy to send and keeps the relationship open.
Beyond job opportunities, informational interviews provide something that no job board, Glassdoor review, or LinkedIn scan can give you: an honest picture of what the work actually looks like. This has implications for your portfolio, your resume, and your interview preparation.
When you've talked to ten data professionals at companies you're targeting, you start to notice patterns:
That's market intelligence. Use it to build portfolio projects that speak directly to those business problems. Use it to sharpen the language on your resume so it reflects how practitioners actually talk about the work. Use it to prepare behavioral interview answers that reference real challenges rather than textbook scenarios.
When you sit down for a formal interview and can say "I've been talking to analysts in this space about the attribution problem, and what I keep hearing is that the challenge isn't the modeling — it's the data quality upstream," you sound like someone who already understands the field at a practitioner level. That's a significant competitive advantage over someone who only knows what they've read in articles.
This exercise walks you through the complete informational interview cycle, from research to follow-up. Plan about two weeks to complete it fully.
Phase 1: Build your target list (Days 1–3)
Identify three companies in your target industry or role type. For each company, find two data professionals on LinkedIn who meet these criteria: individual contributor to senior level, has been at the company 1–3 years, and has some public work or content you can reference authentically.
Document them in a tracking spreadsheet with the connection path for each.
Phase 2: Write and send outreach (Days 4–5)
For each of your six contacts, write a personalized outreach message. Do not use a template — write each message from scratch, grounded in something specific about their work. Send the LinkedIn connection request or email.
Evaluate your messages against this checklist:
Phase 3: Prepare your questions (Days 6–7)
For each company, prepare a set of 8–10 questions drawn from the list in this lesson. Prioritize the ones most relevant to what you know about that company. Choose your top 5–7 for the actual conversation.
Phase 4: Conduct and debrief (Week 2)
When conversations happen, take light notes during and full notes within 30 minutes after. Evaluate each call:
Phase 5: The 24-hour follow-up
Send a specific, personalized thank-you within 24 hours. Set a calendar reminder 3 weeks out for your next touch on each warm contact.
Deliverable: At the end of this exercise, you should have a populated tracker, notes from at least one completed conversation, and a drafted follow-up message. The goal is to get the system running, not to immediately produce a job offer.
Mistake: Asking for an informational interview and then immediately pivoting to "are there any openings?"
This tells the person you weren't really interested in learning from them — you just wanted a referral. You lose all the warmth you were trying to build. Fix: Commit genuinely to the framing. Go in curious. Let the job conversation emerge organically over months, not minutes.
Mistake: Targeting only people who are clearly open to networking (e.g., people who explicitly say "open to connect" on LinkedIn)
Those profiles are often overrun with outreach and your message gets lost. Fix: Target people whose work gives you a genuine reason to reach out, even if they haven't signaled openness explicitly. A specific, respectful message is welcome regardless.
Mistake: Treating the conversation as a one-way extraction
You asked all your questions, they answered, you said thanks and left. Fix: Come with something to offer. Share a relevant article, mention a tool you've been experimenting with, ask for their take on something you're genuinely puzzled by. Make it feel like a peer conversation, not an interview.
Mistake: Following up so rarely that the relationship goes cold, then following up only when you need something
You check in three months later with "hey, I'm still looking for work — know of anything?" The relationship has atrophied and now this feels transactional. Fix: Set calendar reminders for follow-ups at 2–4 weeks, then 2–3 months. Make at least one of those touches value-adding rather than ask-based.
Mistake: Ignoring lower-seniority contacts in favor of going straight for directors and VPs
Directors are harder to reach, often don't have detailed hiring timeline knowledge, and are less likely to personally vouche for entry-level candidates. Fix: Focus most of your outreach on individual contributors and senior analysts. They're more accessible, more candid, and often more influential in the actual hiring decision than you'd think.
Mistake: Failing to ask "who else should I talk to?"
This is the single highest-leverage question in the informational interview, and people routinely forget to ask it. Fix: Make it the last substantive question every time. A warm introduction to a second contact from your first contact is exponentially more valuable than a cold outreach.
Warning
Informational interviews work on a time scale of months, not days. If you're trying to have a conversation on Monday and have a referral by Friday, you're doing it wrong. This strategy requires patience and consistency. The candidates who build relationships before they need them are the ones who benefit when something opens up.
Informational interviews are one of the most underused tools in a data job seeker's arsenal — not because people don't know they exist, but because most people misunderstand what they're for. They're not covert job applications dressed up as friendly chats. They're a way to build genuine relationships with practitioners in the field so that when something opens up, you're already in the conversation.
The mechanics are learnable. Identify the right people. Write outreach that's specific and respectful of their time. Ask questions that actually surface real information about team dynamics and growth. Listen for the signals that indicate hiring is on the horizon. Follow up consistently, offer value, and let the relationship develop naturally. Then, when the moment is right, make a direct and low-pressure ask for a referral.
The compounding effect is real. Ten conversations over three months doesn't mean ten contacts — it means ten potential referral sources, plus whoever they introduced you to, plus the market intelligence that makes every interview you do land harder.
Concrete next steps:
This week: Build your target list of six contacts and write your first three outreach messages. Don't wait until you have the "perfect" message — send a genuine one.
This month: Conduct at least two informational interviews. Debrief after each one and update your tracker.
Ongoing: Set a weekly 30-minute calendar block for "network maintenance." Review your tracker, send follow-ups, reach out to new contacts. Keep the pipeline moving.
As you go deeper: Use what you're learning in these conversations to sharpen your portfolio. If three people at e-commerce companies mention that churn analysis is a constant need, that's a strong signal for your next portfolio project.
When you're ready to evaluate an offer: Use your network to do due diligence on companies before you say yes. The lesson on how to evaluate a data team's technical stack, culture, and growth potential before you accept an offer is a natural companion to this one.
For more on the tactical side of requesting these conversations, including scripts for different connection contexts and timing strategies, see the detailed companion lesson on how to ask for an informational interview with a data professional.
The data job market rewards people who treat the search as relationship-building rather than application-submitting. Informational interviews are how you do that at scale, before you're desperate, when you still have the luxury of being genuinely curious.