Available for 1–2 new engagements · this quarter

We design the data platform — then build the pipelines that run on it.

A boutique studio for teams drowning in brittle ETL. Lakehouse architecture, dbt/Spark pipelines, orchestration and governance — delivered end-to-end, remote, on a direct contract.

Grown out of research on reproducible experimentation — we treat data pipelines like lab experiments: versioned, tested, observable.

Start a conversation → See how we help

End-to-end architecture → pipelinesGoverned tested & observableDirect contract no agency margin

Tools we build with

DatabricksdbtApache SparkSnowflakeAirflowKafkaAzureAWSPython

Two ways to work with us

Architecture first. Then pipelines that ship.

Most teams don't need another pair of hands — they need someone who owns the platform, not just a ticket.

Engagement 01

Architecture & Strategy

A senior, vendor-neutral blueprint for your data stack — grounded in cost and latency, not hype.

Lakehouse / warehouse target-state design
Build-vs-buy and tooling trade-offs
Governance, data-quality and lineage roadmap
A delivery plan your team can execute — or that we execute

Fixed-scope sprint · fractional advisory · retainer

Engagement 02

Build & Implementation

Implementation-as-a-service: the blueprint taken to production, tested and handed over.

Databricks / dbt / Spark builds & legacy migration
Pipelines with testing, CI/CD and observability
Streaming and batch, orchestrated (Airflow/Kafka)
Data contracts and quality gates

Fixed-scope delivery · embedded lead · retainer

Why teams pick us

Pipelines treated like software.

Reproducible, tested, observable — because a data platform you can't trust is worse than none.

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Research-grade rigor

Versioned, tested, reproducible pipelines — an experiment mindset, not copy-paste SQL.

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End-to-end ownership

Architecture, build and handover from one accountable partner. No strategy-deck-then-offshore gap.

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Governed from day one

Lineage, contracts and data-quality gates so the numbers can actually be trusted.

Representative scenarios

Illustrative, not case studies.

Anonymized patterns — the shape of the work, not a claim about any client.

Enterprise · Platform

Legacy ETL → governed lakehouse

Migrated brittle legacy pipelines to a governed Databricks + dbt + Spark platform with automated testing and lineage.

Weeks → hours

Time-to-insight for new data products

SaaS · Quality

Silent breakages → contracts & gates

Introduced data contracts and quality gates so bad data failed loudly in CI instead of silently in dashboards.

Fails loud

In CI, not in the boardroom

Fintech · Streaming

Nightly batch → near-real-time

Moved a critical dataset from a fragile nightly batch to an orchestrated streaming path with monitoring.

Batch → stream

Observable end-to-end

How we work

Low-friction, senior, remote by default.

Direct contract via Deel or B2B — US/EU hours, no third-party margin.

Discovery call

A 30-minute call to understand the goal, the constraints and whether we're the right fit. No pitch deck.

Scope & proposal

A written scope with milestones, deliverables and a fixed price or retainer. You know exactly what you're buying.

Build in the open

Weekly demos, code in your repos, decisions documented. You see progress, not status reports.

Handover

Documented, tested, owned by your team — or kept on a retainer. Your system, not our lock-in.

Fighting your data infrastructure instead of shipping?

Tell us what you're building. If it's a fit, you'll hear back within one business day.

Lakehouse, pipelines, governance & streaming
Remote · US/EU hours · direct contract
Architecture sprint or full build — your call

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Scenarios shown are illustrative and anonymized — no client names or confidential data. Any figures are directional, not audited benchmarks.