Most pipeline work fails quietly. A job half runs, a column changes upstream, nobody notices for a week, and the number in the board deck is wrong. We build the ingestion, modeling, and orchestration layer so the data arrives on time, reconciles to the source, and tells you when it does not. Then we hand it over with the runbooks, so your team can keep it running without us.
Data engineering consultants who build ingestion, modeling, and orchestration that reconciles to source, alerts on failure, and is handed over with runbooks.
Data engineering consulting is the work of building the layer that moves data from source systems into a place it can be analyzed: ingestion, transformation, modeling, orchestration, and the monitoring that proves it ran correctly. It is the plumbing underneath every dashboard and every model, and it is usually the reason both are wrong.
Data engineering consulting builds the ingestion, transformation, and orchestration layer that feeds reporting and AI. Thinklytics builds pipelines that reconcile to the source system, alert when a load fails or a schema changes, and ship with runbooks so the client team owns them after handover rather than depending on the consultant.
Modeled tables with tested logic, so a metric means the same thing in every downstream tool.
Orchestration with dependencies, retries, and alerting. A failed load pages someone instead of going unnoticed.
Reconciliation back to the source. The warehouse total matches the system of record, and there is a check that proves it.
A platform migration for its own sake. If your current stack works, we improve it.
A dashboard project. Reporting sits on top of this work, it is not this work.
A dependency. Runbooks, tests, and documentation are handed over with the pipelines.
Source audit. What systems hold the truth, how they change, and where the current loads break.
Ingestion and transformation built on your existing warehouse, with tests on the logic that matters.
Orchestration with dependency-aware scheduling, retries, and failure alerting to a named owner.
Reconciliation checks against the source system, plus runbooks and handover to your team.
Revenue discrepancy resolved once six conflicting metric definitions were reconciled to one modeled source.
Adverse event processing time after the intake and transformation pipeline was rebuilt.
Annual infrastructure cost after pipeline consolidation and a domain-owned data model.
Reporting data is always a day behind, and nobody can say why.
Loads are scheduled by clock time rather than by dependency, so a slow upstream job silently pushes everything past the deadline.
Transformation logic lives in several tools with no tests and no lineage, so a change in one place moves a figure in another.
There is no reconciliation check, so partial loads and late-arriving records go undetected.
No schema contract and no alerting on change, so the break surfaces in a meeting instead of in a pipeline run.
They build and fix the layer that moves data from your operational systems into a place it can be analyzed. In practice that means ingestion, transformation and modeling, orchestration and scheduling, tests on the business logic, and the monitoring that tells you when a load failed. The dashboards and models everyone talks about sit on top of this work.
Usually not. Most of the problems we are called in for are modeling, orchestration, and reconciliation problems rather than platform problems, and those follow you onto a new platform. We work on the warehouse you already run unless there is a specific reason it cannot do the job.
Analytics engineering is the modeling and testing layer, usually in the warehouse, usually in SQL. Data engineering includes that plus getting the data there in the first place and keeping it arriving: ingestion, orchestration, schema handling, and failure alerting. On smaller teams one person does both, and on this kind of engagement we cover both.
A source audit and a plan is typically two to three weeks. A first production pipeline with tests, orchestration, and reconciliation is usually six to ten weeks depending on how many systems are involved and how much the source data needs cleaning before it can be modeled.
The number of source systems, and the state of the data inside them. A clean API with stable schemas is a known quantity. A legacy ERP with custom fields, inconsistent keys, and a monthly spreadsheet feed is where the effort goes. The transformation layer is rarely the expensive part. Reconciling to a source nobody has audited in years usually is.
You do. Handover includes the code, the tests, the orchestration config, runbooks for the common failures, and a walkthrough with whoever will be on call for it. If you would rather we keep running it, that is a separate managed retainer.
Can you work with our existing dbt, Airflow, or Fabric setup?
Yes, and we prefer to. Replacing working tooling adds risk and cost without changing the outcome. We will say so plainly if a tool cannot do what you need, but that is a smaller share of engagements than vendors suggest.
Scope is set from a source audit before any number is discussed. These are the factors that move the effort.
Inconsistent keys, custom fields, and no documented owner turn modeling into archaeology first.
Proving the warehouse matches the system of record is the step most often skipped and most often needed.
Nightly batch, hourly, and streaming are different builds. Real-time costs more and is rarely required.
Runbooks, tests, and training so your team owns it, versus a build that quietly depends on us.
Every engagement opens with a source audit, so the number reflects your systems rather than an average.
Staff augmentation puts an engineer on your backlog. This is scoped to leave something your team can run.
A working pipeline layer with tests, alerting, and runbooks.
Reports are late, wrong, or disagree with the source system.
You are about to put AI or forecasting on data nobody has audited.
The pipelines are fine and the definitions are the argument: see Semantic Layer Engineering.
You need the governance and ownership model first: see Data Governance Consulting.
The question is which warehouse to buy: see the Data Warehouse Selection tool.
Semantic models and metric definitions on top of the pipelines.