Data engineering in Miami
A company that moves money across the Americas ends up with three versions of the truth: what your ledger says, what the payment provider says it settled, and what the bank statement shows. Add exchange rates that changed between authorisation and settlement, and the monthly close becomes a spreadsheet exercise nobody enjoys.
We build the pipelines that make those versions comparable. Provider files and bank data are ingested on a schedule, stored as append-only snapshots so you can ask what things looked like on any past day, and matched against the ledger with the differences reported rather than hidden. Exchange rates are captured at the moment of each transaction. The pattern is the one we run for FM360, where hourly syncs land in ClickHouse and a verifier proves the dashboard is current.
Miami's software buyers tend to face south. Many of the companies that set up here in the last few years came for the same reason: it is where a US-incorporated business can sit next to its Latin American customers, investors and staff. Cross-border payments, remittances, neobanks, crypto infrastructure and the US arms of Latin American companies make up a large part of the local technology market, alongside real estate, hospitality and trade. The systems they need are rarely built for one country.
That shapes the work. Money movement is the core of the product rather than a feature bolted onto it: idempotent payment calls, a double-entry ledger, reconciliation jobs that compare your records against a partner's settlement file every night and flag the difference. The product has to handle Spanish and Brazilian Portuguese properly, validate local ID numbers and address formats, and settle through rails such as Pix in Brazil and SPEI in Mexico as well as cards. Crypto products add wallets, chain indexers and custody integrations, and the same principle applies: your database has to agree with the chain, and when it drifts, somebody needs a report saying where. Real estate, hospitality and trade bring their own version of the problem, usually a workbook that runs the business and quietly disagrees with its copies.
The hiring market has moved with the migration of finance and technology firms from New York and California. Built In puts the average base salary for a software engineer in Miami at around $145,000, and engineers who are bilingual and have shipped payments work are the hardest seat to fill. That is the seat we fill for a defined build: a payments integration, a reconciliation system, a second-country launch, or the months while a permanent hire is found. Work starts with a fixed-price two-week piece, and the code belongs to you from the first commit.
We move our working day for Florida. Four hours of every working day overlap with your morning in Eastern time, stand-up included, so calls, code review and decisions happen live with the engineer who writes the code. Everything else is written down where you already work, and runbooks and architecture notes are written as we go, so the next engineer who joins can read how the money moves.
We are the right fit for a defined build: a payments integration, a reconciliation system, a second-country launch. Senior capacity starts within days, the first piece is a fixed-price two-week engagement, and we can cover the months while a permanent hire is found. Agencies serving Miami's fintech, hospitality and retail brands can put the work under their own name.
Four ways this arrives.
Two definitions of the same metric, computed in two places. We pick one, write it as a tested model in version control, and point every report at it, so the argument moves from whose number is right to what the definition should be.
A job reports success while the report still shows yesterday. We give every table a written freshness guarantee, monitor what the reader actually sees rather than the job that feeds it, and alert a person when the guarantee is missed.
A CRM, an ERP or a work-order tool reachable only through its API. We build the sync with retries, rate limits and normalisation at the boundary, and keep append-only snapshots so last Tuesday is a query rather than a restore.
Analytical queries running against the application database at the worst possible moment. We move them to a columnar store, keep Postgres for transactions, and feed one from the other by change data capture or scheduled extracts, whichever the freshness requirement calls for.
A nightly work-order extract into append-only ClickHouse snapshots, with a verifier that reads the live Tableau dashboard to prove it shows the newest data.
Read the write-up →Asked by Miami teams.
How do you work with teams in Miami?+
We are in Bengaluru and move our working day for Florida, so four hours of every working day overlap with your morning in Eastern time, stand-up included. Calls, code review and decisions happen in that window. Everything else is written down in your own tools, whether that is Slack, GitHub or Linear, and the person on every call is the engineer who writes the code.
Can you build for Latin American markets as well as the US?+
Yes, and it is more than translation. Mexican and Argentinian Spanish differ, Brazilian Portuguese is its own language, and ID numbers, address formats, currencies and instalment payments all change by country. We design localisation into the first component, format money through the platform's own internationalisation tools, and integrate the payments provider you have chosen, with room for a second provider and local rails such as Pix and SPEI.
How does your rate compare to hiring in Miami?+
Built In puts the average base salary for a software engineer in Miami at about $145,000, before equity and benefits. Our published rate is $35/hour, or $5,400 a month for an embedded engineer, with a $5,000 minimum. There is no recruiting time and no employment overhead, and work starts within days of the call. The first piece is a fixed-price two-week engagement at $2,800, so you judge us on working code.
Can you work white-label for a Miami agency?+
Yes. Miami agencies regularly win fintech, hospitality and ecommerce projects that need more engineering than the in-house team can take on at once. We work under your name as part of your team, so your client deals with you and the work arrives as yours. The NDA comes before any specifics, the code belongs to the client from the first commit, and runbooks and architecture notes are written as we go.
Do we need a data warehouse yet?+
Maybe not. If your reporting runs comfortably against a Postgres read replica, a warehouse adds a second system to pay for and keep in step. The signs you need one: analytical queries slowing the product, data from several sources that has to be joined, or history you must keep that the application overwrites. We will tell you which side of that line you are on.
Why ClickHouse rather than Snowflake or BigQuery?+
Not always. For steady, high-volume analytical work, ClickHouse is fast and cheap to run, self-hosted or on its managed cloud, and we run it in production. Snowflake and BigQuery ask less of you operationally and suit ad hoc querying across a large team. If nobody on your side wants to operate a database, we will recommend the managed option even when the invoice is larger.
What does a freshness guarantee actually mean?+
A written statement per table or report, such as "never more than an hour behind" or "reflects yesterday's close by 07:00". Each one has a check that measures it and an alert that fires when it is missed. Without it, stale data looks exactly like correct data until somebody makes a decision on it.
Can you work with the tools we already have?+
Usually, yes. If you already run dbt, Airflow, Fivetran or a BI tool your team knows, we build inside it. We suggest replacing a tool only when it is the cause of the problem you hired us for, and we show you the evidence before asking you to fund a migration.
How do you handle personal data in the warehouse?+
We decide what should reach the warehouse at all before building the pipeline. Identifiers can be hashed or tokenised at ingestion, sensitive columns restricted by role, and retention enforced by the pipeline itself. Every field is documented from source to dashboard, so anyone can see where a number came from and who can read it.