GTM Engineer, UK Company Data & Outbound Infrastructure (Python, Cold Email)

SCLZY builds and delivers outbound campaigns for B2B firms in finance niches, using free UK public data (Companies House bulk register, officer records, charges data) instead of buying lists, and dedicated cold email infrastructure instead of a client's own domain. We need someone to own the full technical build behind a live campaign: the data, and the infrastructure it sends from. You'll be working from existing, documented Python scripts and build checklists, not building this from a blank page. The pipeline, the known gotchas, and the QA gates before anything goes live are already written down. What you'll do, data: Run and adapt existing Python scripts against the Companies House bulk register (7-part alphabetical snapshot, ~5.7m companies) to build target lists per niche, filtered by size band, sector, charge/borrowing history, and other signals. Find and verify company domains and named contacts (director names via the Companies House officers API), using scripts already built for this. Catch data-quality problems before they reach a send list: wrong size bands, name-match contamination (e.g. a search for "corporate finance" catching banks and lenders, not just brokers), parked domains, stale contacts. What you'll do, outbound infrastructure and client fulfillment: Buy and set up sending domains separate from a client's primary domain. Provision mailboxes (M365), configure SPF, DKIM, DMARC, MX, and a tracking domain on each. Run inbox warmup and monitor sender reputation and blacklist status before a domain goes into live sending. Load the target data and copy into the sending platform (PlusVibe) and launch campaigns against the QA gate checklist. Flag and fix deliverability issues (bounce rate, spam placement) as they come up. What we're looking for: Comfortable with Python (reading, adapting, and running scripts, you don't need to architect a system from scratch, but you do need to understand what the code is doing well enough to catch when the output looks wrong). Experience with CSV/tabular data at scale (tens of thousands of rows), comfortable with basic filtering, deduplication, and sanity-checking a dataset. Hands-on experience with cold email infrastructure: domain setup, SPF/DKIM/DMARC, mailbox warmup, deliverability, this is not optional, it's half the role. Detail-oriented enough to catch contamination in a list, or a warmup issue in an inbox, before either causes a bigger problem downstream. UK company/business data experience is a strong plus but not required if the above is solid. To apply, tell us about a time you caught a data-quality or deliverability problem before it caused a bigger issue downstream (a bad list, a burned domain, a blacklist hit). If you've worked with Companies House data or run cold email infrastructure before, say so. Then answer the screening questions below.