Data pipeline engineering for UK SaaS, fintech, and growth-stage teams. We build Dagster + dbt + Snowflake / BigQuery pipelines with schema contracts, lineage, and freshness SLAs — the observability that catches the broken upstream before your CFO sees yesterday's numbers in tomorrow's board pack.
A data pipeline isn't a cron job someone wires later. It's the spine your board pack, your forecast, and your CFO lean on every Monday morning. We build it like one.
“Freshness went from 78% to 99.9% and the Snowflake bill dropped 64%. The board pack stopped being a three-day fire drill.”
UK data pipelines shipped to production since 2019, all still running.
Freshness SLA on every pipeline. Late data pages before the dashboard does.
Snowflake bill cut after Priya's rebuild. Right-sized warehouses, cost per workload.
Priya runs analytics at a UK growth-stage SaaS. Two engineers maintained 47 cron jobs that fed 12 dashboards. Then one job died silently on a Thursday. Monday's board pack showed last Wednesday's revenue. The CEO asked why the dashboards looked good while the bank balance didn't.
The pipelines weren't the product, so nobody owned them. Her team spent three days reverse-engineering which job had failed and what the truth actually was. No lineage. No freshness alert. No way to trace a number back to its source.
We rebuilt it over twelve weeks. Dagster orchestration, dbt models with tests, schema contracts, freshness SLAs, a lineage UI analysts read in two clicks. The Snowflake bill dropped 64%. This page is for the data lead who decided the numbers have to be trustable, every Monday, without a three-day fire drill.
Each one is the difference between a warehouse analysts trust and one they quietly stop opening.
“A cron died on Thursday. The dashboard showed stale data through Sunday. The CEO asked if the numbers were even right.”
Dagster orchestration + freshness SLA + alerts. Every pipeline orchestrated. Freshness SLA per dataset. Late equals a page. Silent failure becomes architecturally impossible.
“Upstream renamed a column. The pipeline ran. The dashboard showed nonsense. An analyst spent a week tracing it.”
Schema contracts gated in CI. dbt and SQLMesh contracts. An upstream rename breaks the build, not the dashboard. Caught at the pull request.
“Our Snowflake bill went from £4K to £18K in a month. Nobody knew why.”
Per-workload cost attribution + auto-suspend. Per-pipeline cost tags. Auto-suspend on idle. Right-sized warehouses. Median bill cut 60% in the first audit pass.
“A bug from six months ago. Backfilling took three weeks and broke four downstream dashboards.”
Idempotent, reproducible, flag-based backfill. Backfill is a flag. Pipelines are idempotent and reproducible from a clean state. Downstream dependencies tested in shadow mode first.
“An analyst asked where a metric came from. Nobody knew. It took two days to trace.”
Lineage UI from chart to source. Dagster and OpenLineage UI. Click the chart, see the SQL. Click the SQL, see the source. Two clicks, max.
“470 dbt models, 14 marts. Nobody knows which one to use.”
dbt structure + ownership + freshness. Standard source, staging, marts layout. Ownership per model. Freshness SLA per mart. Models nobody queries get deprecated quarterly.
“Analysts wait two weeks for a new metric. The data engineering team is the bottleneck.”
Semantic layer + self-serve catalogue. Cube or dbt Semantic Layer. Analysts ship metrics via PR and CI. Data engineers review, they don't implement.
“The pipeline ran. The metrics look weird. Nobody knows why. Stand-up takes 40 minutes.”
Per-pipeline observability + alerting. Datadog, OpenLineage, freshness SLAs. On-call reads the lineage UI and the metric and knows what happened in sixty seconds.
Dagster and dbt as the spine. Snowflake or BigQuery as the warehouse. The infrastructure that keeps the cost and the freshness working as you scale.
The engineering that makes the difference between a cron job that dies on a Thursday and a system your analysts trust.
Every transform versioned. An upstream change breaks the build, not the dashboard. A lineage UI from source to chart that analysts read themselves.
A late dataset pages you before the dashboard does. PagerDuty and Slack wired. Median time-to-detect under five minutes, not the next board meeting.
Snowflake and BigQuery cost attributed per pipeline. Auto-suspend, right-sized warehouses. Median bill cut 60% in the first audit pass.
The engineers who sign your scope are the engineers who ship. Senior dbt, Dagster, and warehouse hires are rare. You keep everything; IP assigns on commit.
We replaced 47 cron jobs with one Dagster project: dbt models with tests and schema contracts, Snowflake right-sized, freshness SLAs per dataset with PagerDuty alerts, and a lineage UI for analyst self-serve. Twelve weeks.
The honest shape. We bound the risk at week one with a fixed-price audit before any build code ships.
Two senior engineers read your existing estate and brief, then write it all down.
Eight to fourteen weeks of fixed-scope shipping. Typical band £35K to £95K.
One senior engineer, one day a week, for three to six months. Walk away cleanly any time.
“Freshness went from 78% to 99.9% and the Snowflake bill dropped 64%. The board pack stopped being a three-day fire drill.”
— Priya, Head of Analytics, UK growth-stage SaaSPain-first, soft-second.
Snowflake for SaaS-grade governance and multi-cloud. BigQuery when you're GCP-default and ad-hoc heavy. Databricks when ML workloads share the warehouse. We'll recommend at the audit and we're senior on all three, so the pick is about your team and your data, not our comfort.
Dagster for new builds: asset-first, native dbt, lineage built in. Airflow when your team is already Airflow-deep and we'd be fighting muscle memory to move them. Prefect rarely. We map the orchestrator to your team, not the other way round.
The audit is £8K fixed. A build sprint is typically £35K to £95K depending on source count, warehouse scope, and whether you need a semantic layer and reverse ETL. It's fixed-price and scoped at the audit, so you see the number before you commit to the build, not in week ten.
No. We migrate incrementally, one pipeline at a time, behind feature flags. The old and new run in parallel until you sign off. Zero analyst-facing downtime. You're never staring at a dashboard that's gone dark because we flipped a switch.
Snowflake or BigQuery dynamic data masking, row-level access policies, and an audit log per query. UK GDPR and DSAR-ready. The data pipeline engineering we ship is defensible at a SOC 2 review and an ICO query, because the access controls are in the warehouse, not in a spreadsheet of who-can-see-what.
30-day walk-away both ways. Milestone billing 25/25/25/25. UK VAT registered, listed on Companies House, shipping since 2019. You're never more than four weeks at risk of paying for nothing, and everything we build is in your repo and your warehouse from commit one.
Every build has two senior engineers paired, not one. Every decision goes into an ADR the same day. Mohit reviews every PR. In seven years, two engineers have left mid-project; both handovers were inside 48 hours because the work was written down, not held in one person's head.
Yes, with 14 days' notice. Engineers move to other projects, spend pauses, and you resume with another 14 days' notice. No cancellation fee. We've done it six times in the last two years for teams whose priorities moved.
A look at the kind of data pipeline engineering surface we hand over — real screens, real data, documented and yours from day one.
Tell us your current pipelines, your source count, your warehouse, and the deadline you're working to. Mohit replies inside 24 hours: a clear yes, a clear no, or the one question that decides it, plus the next 5-day audit slot.
Yes, no, or the deciding question. Straight to your inbox.
A 30-page brief, six ADRs, a risk matrix, and a fixed-price quote.
Freshness SLAs, lineage, cost attribution, handover your next hire reads.