Data engineering in Los Angeles
A music label, a creator network or a distributor in Los Angeles is paid through statements from dozens of platforms, each with its own format, schedule, currency and idea of what counts as a stream or a view. Turning those into accurate payouts for artists and creators is a data problem, and when it goes wrong it becomes a dispute.
We build pipelines that ingest every platform's statements as they arrive, keep the raw files untouched, and normalise them into one model of assets, rights holders, territories and earnings. Splits are applied from data rather than spreadsheets, and every payout line can be traced back to the statement rows that produced it. We use the same append-only approach we run for FM360, so any past period can be recalculated exactly.
Los Angeles buys software for businesses that make things people watch, wear, listen to and fly. Entertainment and media are the obvious ones: studios, streaming services, post-production houses, music companies, games and the creator economy that grew up around them. Alongside them sit consumer brands selling direct to customers online, a fast-growing aerospace cluster around El Segundo and the South Bay, and the logistics trade that runs through the ports of Los Angeles and Long Beach. The buyer might be a creator with an audience, a studio's technology team or the operations lead at a launch company.
Each brings a different system. Media is heavy: video has to be ingested, transcoded, stored and served, storage and egress costs grow faster than anything else on the bill, and rights and territory windows decide who may watch what, where and when. Consumer brands live with launch-day spikes, when one post or one drop sends a surge of traffic in minutes. Creator platforms need moderation that keeps the product feeling fast. The South Bay's aerospace companies grow quickly and need the commercial software around the engineering: hiring and supplier portals, fan and merchandise stores, and the scheduling and operations tools a company outgrows spreadsheets for.
Built In puts the average base salary for a software engineer in Los Angeles at around $147,000, and studios, streamers and the large technology campuses in the city compete for the same people. Much of the work here also runs to a fixed date: a premiere, a new season, a product drop, a launch window. When the date is set and the team is short, a senior engineer who can start in days on a defined piece of work is often the quickest way to hit it.
We work from Bengaluru with four hours of live overlap every working day, placed across your morning in Los Angeles. Stand-ups, reviews and launch planning happen in that window, with the engineer who writes the code. Everything decided outside it goes into your repository, your tracker and the runbooks we write as we go, so each morning starts with a clear record of what moved.
We fit best around a fixed date: a store before a drop, a media pipeline before a new season, a creator app before launch, or commercial tools for a fast-growing South Bay company. Senior capacity starts in days, usually with a fixed-price two-week piece. Agencies can bring us in white-label, 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 Los Angeles teams.
How do you work with teams in Los Angeles?+
We are in Bengaluru, with four hours of live overlap every working day placed across your Los Angeles morning. That window holds stand-ups, design reviews and launch planning, and you talk to the engineer who writes the code. We work in your Slack, your GitHub and your Linear, so progress shows up where your team already looks. Work finished outside the window is written up there too, ready when your day starts.
Do you build for media and entertainment companies?+
Yes. We build ingest and transcoding pipelines, rights and territory windows held as data, review platforms with expiring, watermarked links for outside collaborators, and archive search that finds a line of dialogue by its timecode. For labels and creator networks, we build the pipelines that turn platform statements into accurate payouts. Each piece is scoped to ship before the premiere, season or release date it is meant for.
How does your rate compare to hiring in Los Angeles?+
Built In puts the average base salary for a software engineer in Los Angeles at about $147,000, before equity, benefits and recruiting fees. Our published rate is $35 an hour, with a $5,000 minimum. You skip the months of recruiting and the employment overhead, and we can start within days. A fixed-price two-week piece at $2,800 is the usual beginning, so you judge us on what ships.
Can you work white-label for a Los Angeles agency?+
Yes. Many Los Angeles creative and digital agencies win the brief and then need engineering behind it. We build under your name: your client sees your agency and your process, and we work inside your Slack, tracker and repositories. We can join client calls in the overlap window or stay behind the scenes. An NDA comes first, and the code belongs to your client from the first commit.
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.