GTM engineer is the fastest-growing title in B2B. Job postings grew over 200% year-over-year in 2025, and the number keeps climbing. The role exists because modern go-to-market runs on automation, and someone has to build and maintain the pipes that connect data to outreach to revenue.
If you're a GTM engineer, you've already figured out the first half of the job: wiring together CRMs, sequencers, and outreach tools. The second half, the part that determines whether any of it actually works, is the data. Specifically, enrichment.
Enrichment is the foundation layer. It feeds lead scoring, routing, personalization, and segmentation. When the enrichment layer is solid, everything downstream works better. When it's not, you get bounced emails, misrouted leads, and sales reps who don't trust the system you built.
This guide covers how to think about enrichment as a GTM engineer: how to evaluate APIs, when to use waterfall enrichment versus a single provider, and how to build enrichment into the workflows your team already runs.
What Enrichment Actually Does in a GTM Stack
A new lead enters your system with an email address. Maybe a name. Maybe a company. That's it.
Person enrichment takes that sparse record and fills in the rest: job title, seniority level, company size, industry, phone number, LinkedIn URL, location, work history. Company enrichment adds the firmographic layer: employee count, industry, growth trends, workforce distributions. Together, these are the fields you need to score, route, and personalize.
Without enrichment, your lead routing rules don't have enough data to fire. Your SDRs waste time researching people manually. Your scoring model can't distinguish a VP of Sales at a 500-person SaaS company from an intern at a startup. Everything slows down.
With enrichment running inline (at the moment a lead enters the system), the record arrives complete. Routing happens instantly. Scoring is accurate from the start. The SDR sees a full profile before they write the first email.
If you want the full technical walkthrough on building this pipeline, including webhook handlers, retry logic, and caching, see Building a Real-Time Enrichment Pipeline with a Person API.
Evaluating an Enrichment API
Not all enrichment APIs are interchangeable. The differences show up at scale, in production, after you've already integrated. Here's what to look at before you commit.
Match rate
What percentage of the emails you send in come back with a match? A good provider hits 60-70% on a typical B2B database. Be skeptical of anything claimed above 90%. High match rates often mean the provider is returning low-confidence guesses rather than verified records.
Test with your own data. Export a sample of 1,000 leads from your CRM, send them through the API, and measure the match rate yourself. The number on a provider's marketing page means nothing compared to what you see against your actual ICP.
Field coverage
Getting a match is step one. Step two is whether the matched record has the fields you actually need. A match that returns a name and company but no job title, seniority level, or phone number is only partially useful.
Ask for the field list. Count how many of the fields your scoring model and routing rules depend on are covered. If your lead scoring weights seniority_level, company_size, and job_function, those fields need to be populated on a high percentage of matches.
Freshness
B2B contact data decays at roughly 2% per month. If the provider refreshes records quarterly, 6% of your enriched data is already wrong by the time the next refresh happens. Look for providers that refresh critical fields (email, title, company) on a weekly or continuous basis.
Ask when records were last confirmed, not just last updated. A record "updated" six months ago might have been confirmed last week with no changes, or it might have been stale for six months. The last_confirmed timestamp tells you more than last_updated.
Latency and rate limits
If you're enriching inline (and you should be), the API response time matters. A 200ms response is fine for a webhook handler. A 3-second response means your form submission feels slow, your webhook times out, or you need to move to async enrichment, which adds complexity.
Rate limits matter too. What's the ceiling? What happens when you hit it? A good API returns rate limit headers (RateLimit-Remaining, RateLimit-Reset) so you can throttle proactively. A bad API just starts returning 500 errors.
Check whether the rate limit is per-second, per-minute, or per-day. Per-second limits matter most for batch backfills. Per-day limits matter for sustained campaign volumes.
Error handling
Does the API differentiate between "no match found" (404) and "something went wrong" (500)? Can you tell the difference between a temporary rate limit (429) and a permanent billing issue (403)? Clean error semantics save you hours of debugging in production.
Waterfall Enrichment: When and How
Waterfall enrichment means chaining multiple data providers in sequence. Send the record to Provider A first. If A doesn't match or returns incomplete data, try Provider B. Then Provider C. The idea is that different providers have different coverage strengths, so stacking them fills more gaps.
Clay popularized this pattern. Their platform makes it easy to build waterfall workflows across multiple enrichment sources. If you're already in Clay, you've probably set one up.
But waterfall enrichment has real tradeoffs that don't always come up in the sales pitch.
Each provider in the waterfall charges per lookup. A three-provider waterfall costs up to 3x per record. If Provider A matches 65% and Provider B catches another 15%, you're paying for 100% of the Provider A lookups plus 35% of the Provider B lookups. The math needs to work for your volume.
Then there's data conflicts. When two providers return different values for the same field (Provider A says "VP of Marketing," Provider B says "Senior Director of Growth Marketing"), you need a resolution strategy. Which provider wins? Do you go by confidence score? Recency? The one that matches your CRM? Without a clear rule, you end up with inconsistent data.
Latency stacks too. Each provider in the chain adds latency. A three-provider waterfall with 200ms per call takes 600ms in the worst case. That might be fine for async enrichment but too slow for inline.
And returns diminish fast. The second provider in a waterfall typically adds 10-20% incremental coverage. The third adds 5-10%. By the time you get to a fourth provider, you're often paying more per incremental match than the match is worth.
If one provider covers 65%+ of your ICP with the fields you need, at the freshness you require, the complexity of a waterfall may not be worth it. A single reliable provider with a clean API, good documentation, and fast support is easier to maintain, debug, and reason about than a four-provider waterfall with data conflict resolution logic.
Evaluate a single high-quality provider first. Add waterfall complexity only when the coverage gap is large enough to justify the cost and maintenance overhead.
Building Enrichment Into Your Workflows
GTM engineers live in Clay, Make, Zapier, n8n, and custom scripts. Here's how enrichment fits into the workflows you're probably already running.
Inbound Lead Enrichment
The highest-impact workflow. A lead fills out a form, and before the record hits your CRM, it's enriched with full profile data.
In Clay, this is a "New row" trigger connected to an enrichment step. In a custom stack, it's a webhook handler that calls the enrichment API before writing to your database.
import os
import requests
def handle_new_lead(lead):
"""Webhook handler: enrich before saving."""
response = requests.post(
"https://api.datalegion.ai/person/enrich",
headers={"API-Key": os.environ["DATA_LEGION_API_KEY"]},
json={"email": lead["email"]},
)
if not response.ok:
# Save raw lead, enrich later via batch
return save_raw_lead(lead)
data = response.json()
person = data["matches"][0]["person"] if data.get("matches") else None
return save_to_crm({
"email": lead["email"],
"title": person.get("job_title") if person else None,
"seniority": person.get("seniority_level") if person else None,
"company": person.get("company_name") if person else None,
"company_size": person.get("company_size") if person else None,
"phone": person["phones"][0]["number"] if person and person.get("phones") else None,
})The fallback matters. If the API is down or returns no match, save the raw lead anyway and pick it up in a batch job later. Never lose a lead because enrichment failed.
Outbound List Building
Your SDR team gives you criteria: "Director+ at SaaS companies, 200-1000 employees, based in the US." You pull a list from a person search API, then enrich each record to fill in contact details.
Some APIs combine search and enrichment in one call. Others separate them. Either way, the output is a list of fully enriched records ready to load into a sequencer.
The key mistake here is building the list and then never refreshing it. If your outbound campaign runs for six weeks, the list should be re-enriched at least once mid-campaign. People change jobs. Emails stop working.
CRM Hygiene Automation
Set up a recurring job (weekly or monthly) that pulls records from your CRM that haven't been enriched in 90+ days and re-enriches them. This catches job changes, company moves, and email decay before your SDRs hit stale records.
import time
# Monthly hygiene: re-enrich stale records
stale_records = get_records_older_than(90) # days since last enrichment
for i in range(0, len(stale_records), 10):
batch = stale_records[i : i + 10]
results = []
for record in batch:
response = requests.post(
"https://api.datalegion.ai/person/enrich",
headers={"API-Key": os.environ["DATA_LEGION_API_KEY"]},
json={"email": record["email"]},
)
if response.ok:
data = response.json()
matches = data.get("matches", [])
person = matches[0]["person"] if matches else None
else:
person = None
results.append(person)
update_crm(batch, results)
time.sleep(1.5) # respect rate limitsLead Scoring and Routing
Enrichment fields are the inputs to your scoring model. seniority_level, company_size, job_function, and company_industry are the four fields that drive most B2B lead scoring.
A typical point-based model:
| Field | Value | Points |
|---|---|---|
| seniority_level | C-suite | 30 |
| seniority_level | VP | 25 |
| seniority_level | Director | 20 |
| company_size | 200-1000 | 15 |
| company_size | 1001-5000 | 20 |
| job_function | matches ICP | 15 |
| company_industry | matches ICP | 10 |
Without enrichment, you're scoring on whatever the lead put in the form, which is often just an email address and maybe a first name. The model has nothing to work with.
Routing follows the same pattern. Enterprise leads (C-suite + 1000+ employees) go to the enterprise AE. Mid-market goes to the mid-market team. SMB goes to the automated sequence. These rules only work when the enrichment data is there.
Common Mistakes
Enriching after the CRM write. If you write the raw lead to the CRM first and enrich later, there's a window where SDRs see incomplete records. They'll either skip the lead or research it manually, which defeats the purpose. Enrich before the write.
Ignoring confidence scores. Good enrichment APIs return confidence scores per field. A phone number with "low" confidence isn't worth dialing. A job title with "medium" confidence might be worth verifying before personalizing around it. Build confidence thresholds into your scoring logic.
Not monitoring match rates. Your match rate will drift as your ICP changes or as the provider's data coverage shifts. Track it monthly. If match rates drop below your threshold, that's a signal to evaluate the provider or adjust your targeting.
Treating enrichment as a one-time event. The lead that was enriched six months ago has data that's already 10-12% likely to be wrong. Enrichment is a continuous process. Build the re-enrichment job. Automate it.
Over-building the waterfall. Don't set up a four-provider waterfall on day one. Start with one provider. Measure match rate, field coverage, and accuracy against your actual leads. Add a second provider only if the gap justifies the cost and complexity.
The Integration That Matters Most
If you get one integration right in your GTM stack, make it enrichment. Scoring, routing, personalization, deliverability. All of it traces back to whether the person and company data is accurate, complete, and current. Person enrichment gives you contact intelligence; company enrichment gives you account intelligence. Most teams need both.
Test it against your own data. If match rates and field coverage hold up across your ICP, the rest follows.