Data Engineering Consulting Services for governed data your teams can use.
We provide data engineering consulting services for teams developing their data platform. We assess selected sources and build pipelines and stores for reporting, analytics and AI, with agreed quality checks and operating responsibilities.
- 150+Projects delivered across 12 countries since 2012
- 100+Senior in-house engineers, six countries
- 5.0Unified rating across 61 client reviews on Clutch
- AWS CertifiedSolutions Architects on the team
- ISO 27001Certified security practice, audited by Quay Audit UK
- ISO 9001Certified quality management, audited by Quay Audit UK
01Overview
Define the first data scope
We start with the report, operational question or AI feature your data must support. An assessment identifies the sources and checks needed, then recommends improvements to existing pipelines or a new build.
- Choose the store around the workloadKnown reporting questions can fit a warehouse; exploration can fit a lake; a lakehouse combines both. We assess whether your existing database already meets the need.
- Agree assessment accessWe sign an NDA before access. Assessments use read-only, least-privilege access agreed with your technical contact. Implementation permissions and production actions are agreed separately.
- Define the operating handoverWe agree the documentation and operating owner for the selected pipelines. Schemas, lineage (where a figure came from) and runbooks are named deliverables when included in scope.
- Adjoining engineering workPlatform integration connects business systems. Cloud migration moves the cloud estate. AI integration connects the model to the application using the data.
02Proof
Why clients choose us for data work
Match the evidence to your next dataset or store by checking the delivered scope and the data consumer it served.
- RWTH Aachen University: data capture for project reportingWe built a time-collection and reporting tool for project leaders. The brief called for low-effort entry: people recorded how their time was distributed, and reports showed the allocation across projects. The client sought database and SQL expertise.~40%more project leaders tracking their employees' time with the delivered tool
- Search features built on usable dataFor meinUnterricht, we worked on relevance, filtering and autosuggest alongside AI tagging and embeddings. The feature engagement covered scoping, building, shipping and measurement. Tagging and embeddings supported the move to vector search.~9%lift in search success rate, measured in a controlled experiment across the feature engagement
- Solus Connect: partitioning an authentication databaseOur Solus Connect authentication-platform case describes horizontal partitioning of the existing database using sharding, which splits one logical dataset across multiple databases. The client had scoped the workflow; we developed the platform against those requirements.Shardedhorizontal partitioning of the existing authentication-platform database
03Compare
Build it with us, hire in-house, or buy a platform?
Choose around the data work you need and who will operate it. An existing platform can supply the foundation; your internal team or unicrew can own the assessment and engineering scope.
| Data engineering with unicrewunicrew | Data engineers in-houseHire | A managed data platformBuy |
|---|---|---|
| Best forA selected assessment, pipeline improvement, migration or store design, with defined outputs and acceptance. | Best forContinuing data work where engineers develop domain knowledge and manage changing priorities inside your company. | Best forWorkloads covered by the platform's supported sources, storage and transformation features. |
| What it asks of youA platform owner, source contacts and agreed requirements for the datasets your consumers need. | What it asks of youRecruiting or assigning engineers with data and domain knowledge, and deciding priorities and operating responsibilities. | What it asks of youChecking source coverage, transformation needs, licence and usage costs, and who handles configuration and incidents. |
| You end up owningThe selected data implementation under agreed code-transfer terms, with documentation and operating responsibilities defined in scope. | You end up owningThe team's priorities, data knowledge and responsibility for maintaining the platform. | You end up owningThe configured datasets and transformations under the product licence, plus the responsibility for their operation. |
Quick self-check
Tick what is true for you. The read-out updates as you go.
0 of 4 true
Define the next use for your data
An initial pipeline or dataset can support a new reporting or AI need. For connections between operational systems, we assess platform integration; for a manual workflow, we assess business process automation.
Talk it throughAssess the affected pipeline or report
With one signal, start by tracing the affected dataset from its source to the report. We assess whether a query, transformation, check or alert needs improvement before defining a wider build.
Talk it throughMeasure before you build
With two signals, commission an assessment of the selected sources and the datasets they feed. The written findings identify issues and compare targeted improvements with changes to the store or pipelines.
Book a discovery callReview the data layer as a whole
Three signals justify reviewing how the sources, transformations and store work together. We prioritize the datasets your consumers need and recommend which parts to improve or replace.
Book a discovery callAssess a staged data programme
With all four signals, assess the architecture and operating responsibilities before choosing a staged improvement or build. Start with a useful dataset and agree its acceptance and operating owner. Most engagements start within two to four weeks.
Start with discoveryWe build pipelines that move records out of legacy systems into a store your team can trust. Schemas, lineage and access controls go in first. That also makes the data AI-ready, so a model can reach it and use it safely.
Andrii BurdaSenior Engineering Manager, unicrew04Capabilities
What we build, from pipelines to governance
We engineer the selected data path, from source records to the store and consumers that use them.
- Data pipeline development
Data pipelines and ingestion
ERP, CRM, APIs, flat filesWe build data pipelines from selected ERP, CRM, supplier and carrier APIs and flat files. The ingestion scope defines batch or streaming needs, source formats, transformations, validation and freshness requirements.
- Cloud data migration services
Cloud data migration
AWS or Azure, in slicesWe plan and move selected legacy data to AWS or Azure. The scope identifies the history to retain, validation samples and checks, and the conditions for switching consumers to the new store.
- Data architecture consulting
Data architecture and scaling
Partitioning, sharding, read pathsWe assess query and read paths alongside storage and partitioning. Indexes, caching, sharding or a different store can each fit a scaling need; the choice follows the workload, data volume and operational requirements.
- Data warehouse and lakehouse design
Warehouses, lakes, and lakehouses
Snowflake, Postgres, Neo4jWarehouses organize data for defined reporting questions; lakes retain data for exploration; lakehouses support both workloads. We assess Snowflake, Databricks, Postgres, MS SQL and relationship-based stores such as Neo4j against your requirements and existing platform.
- AI data readiness
AI-ready data foundations
Schemas, lineage, freshnessWe prepare selected datasets for the AI use case you want to build. Schemas, lineage, access controls and freshness checks define what the model can reach and how the data is maintained.
- Data governance consulting
Data governance and security
Access, encryption, retentionWe define access, encryption, retention and audit requirements for the selected datasets. unicrew holds ISO 27001:2022 and ISO 9001:2015 certifications; application controls are agreed for your scope. A dedicated security engagement is cybersecurity consulting.
- Reporting and analytics engineering
Analytics and reporting enablement
Reports and the tools that feed themWe agree reporting definitions with your analysts and build the datasets and collection tools that feed them. Data-entry design follows how users record information, with validation scoped around the reports it will support.
- Data capture automation
Data capture and entry automation
Recognition, validation, exceptionsWe build document recognition and validation with an operator screen to release or correct captured records, as in a document-recognition tool. We scope workflow execution separately when that is the main purchase.
05Trust
A data layer you can trust
We define acceptance for the selected data deliverables. An early production dataset is included when that milestone is part of the build agreement.
- Written firstA map of selected sources and identified issuesWith the fixes ranked by what each one unblocks
- In the scopeSchemas, lineage and runbooks as named deliverablesSelected schemas, lineage and runbooks, agreed for handover
- EarlyA first trusted dataset in productionNot a demo environment, and not at the end
06Stack
The stores and tools we build on
We select the stack around your workloads, existing cloud and the engineers who will maintain it. The store choice follows reporting, exploration and processing needs.
Python
C#
ASP.NET
Snowflake
Databricks
Apache Spark
PostgreSQL
MySQL
MS SQL
MongoDB
Neo4j
Elasticsearch
AWS
Azure
Google Cloud07Engagement
Ways to start a data project with us
Choose whether to assess a data path, deliver selected pipelines and a store, or develop a continuing data roadmap.
An assessment first
Decision firstWe assess selected sources, pipelines and stores against the consumer requirements. The written findings recommend improvements and define a first implementation scope. Commissioning the build is a separate decision.
- Best when
- You need to establish the condition of selected data sources and pipelines
- You pay
- Billed hourly, quoted per project
- Typical start
- Two to four weeks
A scoped build
Defined scopeWe build against named sources, a chosen store and agreed acceptance criteria. The estimate follows the defined transformations, checks and delivery requirements.
- Best when
- The pipeline, store improvement or migration has defined outputs and acceptance
- You pay
- Outcome based, quoted per project
- Typical start
- Two to four weeks
A dedicated team
Long-runA senior engineering team develops the data layer as your roadmap grows. We agree responsibility for new sources, changes to datasets and the operation of selected pipelines.
- Best when
- You have a continuing data roadmap and want a team responsible for its agreed scope
- You pay
- Billed monthly, per team member
- Typical start
- Two to four weeks
Assess the platform, then choose the work
An assessment traces the selected pipelines and reports through the platform you already run. You receive written findings and prioritized changes before deciding on implementation. We agree continued maintenance separately, and there is no minimum engagement period.
08Industries
Industries we do data work in
We define data around the decisions it supports, including shipments, guest activity, financial records and learning content.
Logistics and transportation
Lead, job-order, document, billing and call-center data all have to resolve behind one customer record, as they do on a US moving-company SaaS built from scratch.
Hospitality and leisure
Booking, membership and payment systems that each hold a piece of the same guest, including a venue platform whose operators need one view of them.
Fintech and accounting
Financial records need defined relationships between transactions and invoices. An accounting platform taken over mid-build included automatic reconciliation, with further accounting software work shaped around those records.
Commodity trading (CTRM/ETRM)
Positions, curves and settlement data need consistent definitions across trading systems. We built a B2B commodity trading platform with its CRM integrated into the marketplace. These definitions also matter in energy and utilities.
EdTech and learning
Search uses the records describing curated learning material. On a German edtech platform, we worked on tagging and embeddings alongside user-facing search features.
E-commerce
We engineer selected product, pricing and order data flows between storefronts, appropriate sales channels and the ERP, as in our work for a European manufacturer selling into seven EU countries.
Plan the data behind your next report or AI feature
Bring the report or feature you want to support and the systems holding its data. We identify the source information needed to scope an assessment, pipeline improvement or build.
What happens after you contact us
- We reply within one business dayWe identify the source-system information needed to assess the work you described.
- A call about your sources and data consumersWhich systems hold the records, who uses them, and the volume, history and freshness the output needs.
- A written assessment of selected sourcesAn agreed assessment identifies data issues and prioritizes changes against the report or feature they support.
- Then an architecture, and a start dateThe store, selected pipelines and acceptance criteria define the build scope. Most engagements start within two to four weeks.
09Delivery
How a data engineering project runs
We agree the data delivery plan around source access, field definitions and acceptance samples. Your technical and analyst contacts confirm the inputs at each stage.
- Discovery and data assessmentWe assess selected sources, their record definitions and the datasets they feed. You provide source contacts, sample data and consumer requirements. We sign an NDA before access and agree read-only, least-privilege assessment access with your technical contact.You getA map of selected sources, identified data issues and prioritized changes.
- Architecture and designWe design the selected store, transformations and governance, including improvements to the existing platform. Your source and consumer owners confirm field meanings, volumes, history, retention and freshness requirements. We record the choices and agree implementation permissions separately.You getA data architecture and implementation scope, with the reasoning and acceptance criteria recorded.
- Development in slicesWe build the agreed pipelines and datasets in increments, with ETL or ELT transformations (before or after loading) suited to the store. Your team supplies the agreed test environment and reviews outputs against the consumer requirements.You getWorking pipelines or datasets and check results for the agreed increment.
- Validation and handoverWe validate the selected data against agreed samples, history and acceptance rules, then plan cutover where included. Your engineer or analyst reviews the results. We agree the documentation and operating handover for the selected scope.You getAcceptance results, the agreed documentation and any scoped cutover plan.
- Support and optimizationWe agree who handles alerts, incidents, source changes and maintenance after delivery. Your team nominates the operating owner; continued unicrew support is scoped separately. Internal operation and a continuing engineering engagement are both options.You getA post-launch warranty period on fixed-scope work, its length agreed at contract.
10Client voices
Our clients say
We saw around a 40% increase of how many project leaders are tracking the time of their employees instead of making guesses or delegating the task. Their response time was fantastic. They were organized and provided us with tools to track their progress in a transparent way. Every time we needed to add some small things to make it work better, they adapted those requirements very quickly.
Our headline result was a controlled experiment showing a roughly 9 percent lift in search success rate. The features built are live in production, and the AI tagging and embeddings work set up our move to vector search. What stands out most is their ability to own work end to end, from user-facing search features to the AI and data layer underneath.
They always seem to have a suitable developer on standby when we require an additional skillset and they always respond very quickly and help to work out a solution if e.g. the project requirements are suddenly changing.
We were able to get the work completed in the expected time frame. There were little to no defects which was very nice because it allowed us to release and move on to our next project without having to back peddle. They were very accessible and took the time to understand our needs. They truly felt like part of the team.
We’ve seen enhanced customer satisfaction thanks to unicrew’s work. Our customers love the UI and speed of the application. We’ve received positive feedback, and there are no errors in the system. unicrew meets their deadlines and stays on budget.
11Case studies
Our case studies
Explore the time-collection and project-reporting tool we built for RWTH Aachen University. Earlier engagements were delivered under our former Artelogic brand.
See all case studies
12Questions
Questions about data engineering projects
Data engineering scope depends on the selected sources and the requirements of the people and systems consuming the output.
Data engineering services collect, transform and store selected data so reports, applications and AI features can use it.
We assess the source systems and engineer data pipelines, warehouses or other stores around the consumer requirements. Record definitions, quality checks, access rules and operating responsibilities are agreed in scope. The first purchase can be an assessment of existing pipelines or a defined new dataset.
We quote the selected data scope after assessing its sources and delivery requirements.
Sources, data condition, volume and history, transformations, checks and operating requirements shape the estimate. The billing model follows the engagement:
- Time and materials is billed hourly and quoted per project.
- Fixed price is outcome based and quoted per project.
- Team extension is billed monthly per team member.
There is no minimum engagement period.
We agree the first useful milestone and its acceptance criteria when we scope the work.
An assessment can provide findings and prioritized changes; a build can deliver a pipeline or dataset for a selected report or feature. Source access, data condition and dependencies determine the schedule. Most engagements start within two to four weeks; that is a start window, and the delivery schedule is agreed separately.
We assess how selected ERP, CRM, API and exported-file data can be read and transformed into the dataset you need.
Your technical contacts confirm available interfaces, permissions, formats and record meanings. Published work includes an EDI tool collecting orders for a food producer and lead, job-order, document and billing records on a moving-company SaaS.
When the purchase is connecting operational systems, we scope platform integration.
Yes. We assess and build the data layer on AWS, Azure or Google Cloud around your existing platform.
The scope defines the services, permissions and stores involved, along with the requirements of the team operating them. A move to another cloud is a separate cloud migration scope when it serves the project.
No. We can scope AI-ready data foundations around the selected datasets a bounded AI use case needs.
We agree quality, schemas, lineage, access controls and freshness requirements for that use. On a German edtech platform's search team, tagging and embeddings supported the vector-search move alongside user-facing feature work. When the model also needs integration into an application, we define that adjoining scope together.
Yes, where the assessment shows the existing pipelines can meet the agreed requirements with targeted improvements.
We trace transformations, quality checks and failure handling, then compare changes to the current implementation with replacing a component. The recommendation follows the sources, workloads and maintenance needs. An existing data platform can remain the foundation for either approach.
We agree failure alerts, freshness checks and a named recipient for the selected pipelines in scope.
The operating agreement defines which events trigger an alert, who investigates, and who restores or reruns the affected load. Your internal team can own that response, or continued unicrew support can be agreed separately. Monitoring and incident responsibilities are acceptance decisions alongside the data checks.
We agree data governance and security controls for the selected sources, stores and consumers against the requirements you provide.
Access, encryption, retention and audit requirements are defined in the design and acceptance criteria. unicrew holds ISO 27001:2022 and ISO 9001:2015 certifications, renewed through a multi-stage audit with Quay Audit UK. Those credentials cover our organization; they do not certify your platform's legal compliance.
Compare the assessment output, implementation acceptance, operating handover and evidence of relevant work.
We make those decisions concrete before the engagement:
- An assessment can be commissioned independently and returns findings for the selected sources. The build is a separate decision.
- The implementation scope names datasets, transformations and acceptance checks.
- Handover names the agreed documentation, including selected schemas, lineage or runbooks, and the operating owner.
- Our client testimonials link to their original reviews. Our ISO 27001:2022 and ISO 9001:2015 certifications were audited by Quay Audit UK.
A data engineering engagement fits when you need a usable dataset, stronger pipelines or a store designed for a new workload.
Bring a defined report, analytics question or AI use case, source contacts who can explain the records, and an owner who can agree acceptance. An assessment establishes the first scope. It can lead to improvements on the existing platform, a defined build or a continuing data engineering team.