Time since we first saw it
Day 11
Similar jobs usually come down between day 6 and day 47.
From 1,409 similar postings that came down. Coming down is not always a hire.
London · London, United Kingdom
Actively hiring, first seen by us 11 days agoLocation type: On-siteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: FinanceFrom the employer’s posting
Deliver datasets behind month-end close and regulatory reporting on time
Turn multi-day manual work into scheduled pipelines and reusable dbt models
Build reports and reconciliations as each product and market launches
Build tests and alerts that catch reconciliation breaks and stale data
Build models so people and AI agents can answer repeat data questions
deliver the datasets behind month-end close and regulatory reporting, on time, every cycle
turn multi-day manual work into scheduled pipelines, runbooks and dbt models
build reports and reconciliations as each product and market goes live
build tests, checks and alerts that catch reconciliation breaks and stale data
build models so people and AI agents can answer repeat questions themselves
1-2+ years in a data or analytics role
A technical or quantitative degree, or equivalent experience
Strong SQL and experience wrangling messy, complex data
Some experience automating repetitive work (e.g. scripts, scheduled jobs)
The right to work in the UK
Actively hiringFirst seen by us 11 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 11
Similar jobs usually come down between day 6 and day 47.
From 1,409 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
The right to work in the UK Nice to have: startup or scale-up experience, Python or R, dbt, Looker or Mode, and something you’ve built, at work or on the side, because the manual way annoyed you. Sound like you? Apply, even if you don’t tick every box. We’d love to meet you.
A technical or quantitative degree, or equivalent experience Strong SQL and experience wrangling messy, complex data Some experience automating repetitive work (e.g. scripts, scheduled jobs)
The job You’ll own the external reports the business can’t operate without, then automate them into reliable, reusable dbt models. From day one you’ll work with Finance, Compliance, Treasury, Product and Data Engineering to make sure every number is modelled correctly. - Own external reporting: deliver the datasets behind month-end close and regulatory reporting, on time, every cycle.
- Own external reporting: deliver the datasets behind month-end close and regulatory reporting, on time, every cycle. - Automate: turn multi-day manual work into scheduled pipelines, runbooks and dbt models, so it only gets done once. - Get ahead of launches: build reports and reconciliations as each product and market goes live, before anyone asks.
Grow as fast as you’re able. We hire for potential, not years of experience. On a lean, talent-dense team, you’ll own real work early and take on more the moment you’re ready. Go from querying data to designing systems. Data, product or business analyst? You don’t need to be an analytics engineer yet. Build the pipelines behind the numbers, and learn dbt, testing, automation and AI tooling from senior analytics engineers. Don't get boxed in. Own the reporting, automate it, then take on our biggest problems wherever they sit. Your hiring manager started in Finance and has since worked across Partner Ops, Fraud, Compliance, Reconciliation and Product.
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Yes. We’ve followed one other Analytics Engineer job there since August 2026. It came down after 67 days.
We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Plenty of good analysts hit the same wall: you query the data but never build the pipelines, you’re boxed into one lane, and more scope means waiting your turn. Here, it’s the opposite.
Taptap Send moves billions of dollars a month across 6 of 7 continents. We’re probably the largest, fastest-growing money movement business you’ve never heard of. Cards, wallets, business-to-business payments, institutional payments, and new markets are all launching now, and each needs its data, reconciliations and reporting built from scratch. That’s your work.
You’ll own the external reports the business can’t operate without, then automate them into reliable, reusable dbt models. From day one you’ll work with Finance, Compliance, Treasury, Product and Data Engineering to make sure every number is modelled correctly.
Own external reporting: deliver the datasets behind month-end close and regulatory reporting, on time, every cycle.
Automate: turn multi-day manual work into scheduled pipelines, runbooks and dbt models, so it only gets done once.
Get ahead of launches: build reports and reconciliations as each product and market goes live, before anyone asks.
Be first line for data requests: build models so people and AI agents can answer repeat questions themselves.
Keep data trustworthy: build tests, checks and alerts that catch reconciliation breaks and stale data before they become incidents.
Share what you know: peer-review code and join our weekly meetups.
Grow as fast as you’re able. We hire for potential, not years of experience. On a lean, talent-dense team, you’ll own real work early and take on more the moment you’re ready.
Go from querying data to designing systems. Data, product or business analyst? You don’t need to be an analytics engineer yet. Build the pipelines behind the numbers, and learn dbt, testing, automation and AI tooling from senior analytics engineers.
Don't get boxed in. Own the reporting, automate it, then take on our biggest problems wherever they sit. Your hiring manager started in Finance and has since worked across Partner Ops, Fraud, Compliance, Reconciliation and Product.
See the whole business. You’ll follow a transfer from the moment a customer taps send, through partner payout and reconciliation, to the report a regulator reads.
Own numbers that matter. Auditors and Finance rely on what you build. This isn’t a dashboard role.
Put more money in families’ hands. Remittances are a lifeline for millions of families and are worth more than all foreign aid. Every report you automate helps us keep transfers cheap and reliable.
1-2+ years in a data or analytics role
A technical or quantitative degree, or equivalent experience
Strong SQL and experience wrangling messy, complex data
Some experience automating repetitive work (e.g. scripts, scheduled jobs)
A strong sense of ownership: when something is broken, ownerless or late, you step in, fix it, and make sure it doesn’t happen again
Reliability under deadlines, a “no task too small” attitude, and comfort chasing stakeholders
The right to work in the UK
Nice to have: startup or scale-up experience, Python or R, dbt, Looker or Mode, and something you’ve built, at work or on the side, because the manual way annoyed you.
Sound like you? Apply, even if you don’t tick every box. We’d love to meet you.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
London, United Kingdom
Working pattern and location restrictions need checking in the full posting.
Reliability under deadlines, a “no task too small” attitude, and comfort chasing stakeholders The right to work in the UK Nice to have: startup or scale-up experience, Python or R, dbt, Looker or Mode, and something you’ve built, at work or on the side, because the manual way annoyed you.