Data engineering services
pipelines that hold.
AI and analytics are only as good as the data underneath. And the data is usually the problem. Scattered across systems. Half-documented. Duplicated. Out of date the moment you query it. Data engineering services fix the foundation. We build the pipelines that pull data from your systems, clean and reconcile it, and keep it fresh. So the dashboard and the model work from reality, not guesswork. For SaaS, healthcare, and data-heavy teams.
Scoped estimate in 3 to 5 days. No obligation, NDA on request.
“They were absolutely phenomenal. The team put in a lot of work to break down what was required of the project and gave an excellent presentation on the process. I highly recommend them and will be working with them again in the future.”
Kayode Leonard
Founder, Project Wolf
Selected clients and shipped projects
Awesome Kyiv
Shelfit
Who you work with
We have moved data that refused to line up
HighCraft is a senior team that pairs full-stack engineering with applied AI for healthcare, SaaS, and expert-led businesses. We have earned Top Rated and a 100 percent Job Success Score on Upwork, one five-star delivery at a time.
We have done the hard version of this. One booking platform pulls from three upstream systems over REST and SOAP. It syncs twenty-three kinds of record and merges twenty-one of them across sources automatically. One inventory platform validates an import row by row and hands back an error-highlighted copy, tested past a million records. You work with the engineers who built those pipelines, not a sales layer in front of them.
2 weeks
idea to working prototype
End to end
prototype to production
Senior
engineers, no handoffs
The quiet failure mode of data work is the pipeline that rots. A one-off script loads the data once. Three weeks later it breaks silently. It feeds a dashboard everyone slowly stops trusting. We build the durable kind instead, incremental and tested, on cloud data infrastructure that follows the patterns Microsoft Azure's data architecture guidance documents. Checks catch bad data before it reaches a report, not after someone has acted on it.
What we actually build
What data engineering has to get right
The plumbing a dashboard hides and a model depends on.
Ingestion from systems that disagree
Pulling data out of the CRMs, databases, APIs, and spreadsheets it lives in. Each has its own format and its own idea of a customer. We have synced and reconciled records across systems that did not agree on so much as an ID. Getting data in is the easy half. Getting it to agree is the work.
Cleaning, matching, and deduplication
Normalizing formats, matching the same entity across sources, and merging duplicates without losing the real differences. On one build we merged twenty-one kinds of cross-source record by normalized name and company. The dedup logic is where data quality is won or quietly lost.
Pipelines that stay fresh
Scheduled, incremental pipelines that keep the warehouse current. They fail loudly when a source breaks, with change tracking so you see what moved and when. A dataset that was right last month and silently went stale is worse than none. People still trust it.
The data layer AI actually needs
Retrieval indexes, embeddings, and clean structured context. So an LLM answers from your real data instead of guessing. Most AI projects stall on the data, not the model. We build the substrate first. A capable model on bad data is just a confident wrong answer.
When you do not need a pipeline yet
If your data fits in one system and a built-in report answers the question, you do not need custom data engineering. We will say so. This work earns its cost elsewhere. When data is scattered across systems that do not talk. When the volume or freshness outgrows a spreadsheet. When an AI or analytics product depends on the data being right. We build the foundation when there is real weight to put on it, not before.
100%
Job Success on Upwork
5.0
Average client rating
Top Rated
Agency on Upwork
11 yrs
Engineering leadership
HIPAA
Aligned delivery
Recognition
Awards and accreditations
Verified on Upwork and recognized by independent agency directories.
Built for the rules healthcare runs on. Practices documented, not implied.
Security & trustAI Prototype Sprint
Validate the workflow before you fund the platform.
A two-week sprint that turns a complex workflow into a working prototype, architecture direction, and a build estimate you can act on.
- Working prototype
- Workflow map
- Architecture recommendation
- AI opportunity and risk assessment
- Delivery roadmap
- Fixed or phased build estimate
Two weeks, one fixed scope. You own everything we build, whether or not you continue.
Week 1
Discover the workflow, build the spine
Week 2
AI where it pays back, then prototype + estimate
Engagement models
Four ways to engage, and a low-risk way to start
We fit the model to the project and the risk, not to our invoice. Most clients start with a two-week discovery sprint that turns the idea into a working prototype and a real estimate, then move into whichever model fits the build.
Time and materials
You pay for the hours you use, billed weekly or monthly. The right call when scope is still moving and you want to steer as you go.
Dedicated team
A senior team embedded with yours and billed monthly, scaling up or down as the roadmap changes. Built for ongoing work, not a one-off.
Fixed price
Agreed scope, agreed price, agreed date. Works when the requirements are already clear and you want certainty before you sign.
Fixed milestones
Phased delivery, paid one milestone at a time. A way to take on a larger build and de-risk it stage by stage.
What clients say
Clients trust us with messy, real-world software
From regulated healthcare workflows to payment-heavy platforms and internal business systems, the common thread is delivery that survives production.
Alex and his team built the core of our Healthcare SaaS. Their grasp of HIPAA and GDPR was crucial for our telemedicine features, and they added AI into the EMR so providers could make better data-driven calls. They know the Microsoft stack and held to WCAG 2.1 throughout. For a healthcare product that needs regulatory care and real engineering, HighCraft.io is the partner you want.
Oleg Shumar
Owner, GetTrusted.io
They were absolutely phenomenal. The team put in a lot of work to break down what was required of the project and gave an excellent presentation on the process. I highly recommend them and will be working with them again in the future.
Kayode Leonard
Founder, Project Wolf
Really enjoyed working with HighCraft.io. They are true professionals that know how to get things done. They were hardworking and skillful, exactly what we were looking for.
Maxim Grossman
Executive, Enigmex Technologies
HighCraft team did a great job creating a brand new site for my company, and I am loving it. It is exactly what I wanted and the team were true professionals and very nice to work with.
Alina Virstiuk
Founder, AwesomeKyiv
What we do
Three ways we turn complex workflows into working software
Start with a prototype, add AI where it creates leverage, or build the full production platform.
Working prototypes
A working prototype built around the real edge cases, so you can validate scope before funding a full build. The cheapest way to find the edge case nobody mentioned.
AI-enabled features
AI inside the product you already run: intake, search, summarization, classification, recommendations, or workflow assistance, with evaluation and guardrails. Built so a real user opens it twice.
Production platforms
Custom platforms built for real users: integrations, permissions, billing, audit trails, and maintenance. HIPAA-aware where it has to be.
Free vendor-risk check
Before you build, check the risk first.
Answer a few plain-English questions and get a vendor-risk read on ownership, proof of work, data exposure, and handover gaps before you fund the build.
- Takes about 3 minutes
- Built for vendor decisions
The page shows the first risk instantly. Email sends the full report.
Related work
What clean data makes possible
The products that only work once the data underneath is right.
Selected work
Software that works, in production
Our clients get to focus on their business, instead of babysitting the stack that holds it together. Client cases below are anonymized where compliance demands; the rest ship under their own names.
How we build
How we build AI workflows that stay controllable
Agentic does not have to mean opaque. We put the controls where the risk is: permissions, approvals, and audit around every AI-assisted step.
Frontend
The product your users and staff actually work in.
API
Typed contracts and validation at the boundary.
Workflow engine
The deterministic spine: states, rules, and handoffs.
Agentic workflow layer
Inspects context, suggests next steps, and triggers tools, with human approval where it matters.
AI / LLM services
Models behind evaluation and fallback logic, not raw and unchecked output.
Integrations
EMR, Stripe, CRM, scheduling, and internal APIs.
Audit, monitoring, permissions
Every AI-assisted step logged, observable, and role-gated.
Controls, not black boxes
- Human approval for sensitive actions
- Tool calls scoped by permissions
- Audit logs for every AI-assisted step
- Evaluation and fallback logic, not raw model output
- Role-based access throughout
- Observability in production
- Integration with EMR, Stripe, CRM, scheduling, or internal APIs
FAQ
Hiring a data engineering team
What buyers ask before they start.
What are data engineering services?
They are the work of getting data from where it lives into a clean, reliable, queryable form. Building pipelines. Integrating sources. Cleaning and matching records. Keeping a warehouse fresh. Analysts and models get the credit. But they only work when the data underneath them is correct and current. That foundation is the service.
What is the difference between data engineering and data science?
Data engineering builds and maintains the pipelines and storage that make data usable. Data science analyzes that data to find patterns and build models. One lays the track, the other runs the train. A data scientist with no clean data spends most of their time doing data engineering badly. That is the usual reason a project stalls.
Do we need data engineering before an AI project?
Almost always, yes. Most AI projects stall on the data, not the model. It is scattered, dirty, or not in the shape a model needs. We build the ingestion, cleaning, and retrieval layer first. A capable model on bad data just produces confident wrong answers faster.
What does a data pipeline actually do?
It moves data from a source to a destination on a schedule, transforming it on the way. Extract from an API or database. Clean and reshape it. Load it somewhere queryable. A good pipeline is incremental, tested, and observable. It stays correct as sources change, and tells you loudly when one breaks.
Which tools and platforms do you work with?
We build on cloud data infrastructure, mainly Azure, with SQL and NoSQL stores. We integrate the sources you already run over their APIs. We pick the stack from your data and team, not a house preference. And we keep the pipeline observable, so you are not flying blind. Where you already have a warehouse, we build into it rather than replacing it.
How much do data engineering services cost?
Send the sources, the volume, and what the data has to feed. We reply with a scoped estimate, usually within 3 to 5 business days. Cost tracks the number of sources, how messy they are to reconcile, and how fresh the data has to stay. You can work hourly, fixed price, or as a dedicated team.
When are you not the right fit?
If your data lives in one system and its built-in reporting answers your questions, you do not need custom pipelines. We will say so. We are also the wrong call for a single one-time data export. We earn our cost when data is scattered across systems that disagree, the volume or freshness has outgrown a spreadsheet, or a product depends on the data being right.
Start a project
Tell us about your project
Send the shape of the problem, even if the requirements are still blurry. We reply with a scoped estimate, usually within 3 to 5 business days. No obligation, NDA on request.
- A senior engineer reads every brief, not a sales rep.
- If an off-the-shelf tool fits better, we will tell you.
- NDA on request before you share anything sensitive.
Prefer email? Write to business@highcraft.io
Rather talk it through? Book a 30-minute estimate review
“Alex and his team built the core of our Healthcare SaaS. Their grasp of HIPAA and GDPR was crucial for our telemedicine features, and they added AI into the EMR so providers could make better data-driven calls. They know the Microsoft stack and held to WCAG 2.1 throughout. For a healthcare product that needs regulatory care and real engineering, HighCraft.io is the partner you want.”
Oleg Shumar
Owner, GetTrusted.io