Your pipelines break on Monday mornings, the cloud bill climbs every quarter, and nobody can say which of three dashboards is right. We audit the data platform you already run, design the one you need next, and give you a roadmap your engineers can actually build from.
We look at your pipelines, storage, orchestration and costs the way a new senior hire would in their first month, only faster and with a written report at the end.
Once we know what’s there, we design what should be there: storage layers, integration patterns, batch versus streaming, and where governance lives.
Snowflake or Databricks? Airflow or Dagster? Fivetran or custom ingestion? We score the options against your data volumes, team skills, budget and compliance rules.
Legacy ETL jobs tend to grow until nobody wants to touch them. We review the code, schedules and dependencies and tell you what to keep, refactor or replace. Where the plan calls for new flows, our data pipeline development team can build them.
Most data incidents are process problems: no tests, no CI, no owner. We set up the practices that catch bad data before it reaches a board report.
AI projects stall when the data underneath them isn’t ready. We check whether your data can support the use case you’re funding and what has to change first.
A fixed-scope review of your current platform, usually two to four weeks. You get the findings, the risks and a prioritized backlog.
For teams planning a new platform or a big change. We design the target state and phase the work into releases you can budget for.
When you’d rather not hand the plan to someone else, the same engineers who designed it build it, through our data engineering services.
Our consultants are working data engineers. The architecture they draw is one they’d be willing to ship and support themselves.
We work across Snowflake, Databricks, BigQuery, Fabric and open-source stacks, so the advice isn’t tied to a resale quota.
When the plan moves to build, the Relevant AI Delivery Framework™ cuts delivery time by up to 50% with up to 20% fewer bugs through AI-assisted development, and every release still passes senior code review.
ISO 27001-certified processes, with GDPR and HIPAA requirements built into the architecture from the first review.
99% of our projects are delivered on time, so the dates in your roadmap are dates we plan to hit.
96% employee retention means the people who audit your platform are still around when it’s time to fix it.
We agree on what the business needs the data platform to answer and which decisions this engagement should unblock. If the bigger picture is still open, data strategy consulting settles goals and priorities first.
Interviews with data owners, engineers and the people who read the reports, plus read access to code, orchestration and cloud billing.
Findings come back with impact, effort and a recommended fix for each, so nothing ends as a vague list of concerns.
We present the design and the options we rejected, then revise it with your team before anything is final.
Releases with dates, owners and a first milestone that delivers value within a quarter.
Your team takes the plan, or ours builds it. Either way, you keep every document and decision record.
Usually four things: an assessment of the platform you run today, a target architecture, a tech stack recommendation and a roadmap to get there. Some teams add DataOps setup, data quality rules or an AI readiness check. If you already know what’s wrong, we can skip straight to design. If you don’t, start with the audit, since it’s the cheapest way to avoid fixing the wrong thing.
It depends on four variables: how many sources and pipelines we need to review, how many teams we interview, whether you need a proof of concept, and whether we stay on to build. A fixed-scope audit is the most predictable option. Contact us with your source count and main pain points, and you’ll get a range for your setup rather than a generic quote.
An audit typically takes two to four weeks. Architecture and a roadmap take four to eight weeks, depending on how many systems and stakeholders are involved. You’ll get interim findings along the way, so you won’t wait until the end to hear about a serious risk.
Three signals come up again and again: pipelines fail often enough that people stop trusting reports, cloud data costs grow faster than usage, or a big decision (new platform, AI program, acquisition) is coming and nobody in-house has done it before. Any one of them is reason enough for an outside review.
Data engineering builds and runs the systems that collect, clean and serve data. Data science uses that data to build models and answer questions. Without solid engineering underneath, data scientists spend most of their week fixing inputs instead of modeling.
There’s no universal winner. Snowflake is strong for SQL-heavy analytics with little ops work. Databricks fits teams with heavy Spark, ML or streaming workloads. Fabric makes sense if you’re deep in the Microsoft stack and Power BI. We compare them on your workloads and costs, and run a proof of concept when the gap is small.
Yes. We migrate in slices: one domain or source at a time, with old and new pipelines running side by side until the numbers match. Reports keep working throughout, and the cutover for each slice is a short, planned event.
They join your channels, rituals and code review. The goal is to leave your engineers more capable, not dependent on us. If you need more hands later, you can hire big data engineers to extend the team.
A two-week look from outside is often enough to see why it doesn’t.
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Healthcare
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