Data Engineering Services
Build a data foundation that makes analytics and reporting trustworthy today, and powers the automation of tomorrow. With our data engineering services, your AI agents act on records that are resolved to one version, validated on the way in, and traceable back to their source.
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The real challenges behind data-driven failure
With agentic AI, a wrong number that used to be caught in a meeting now reaches a customer. Sensitive data that was merely untidy sits one access request away from a compliance failure. These are the challenges we solve as a data engineering services company.
Your data sits in systems that were never meant to join
The CRM has its own customer ID, billing has another, the product database a third. Formats and refresh cadence differ too, so someone exports to a spreadsheet, matches rows by hand, and that spreadsheet becomes the integration layer nobody has documented.
Every system has its own version of the same number
Ask two teams for active customers and you get two numbers, both correct under their own definition. Every downstream metric inherits the argument. The ambiguity a human would have raised in a meeting persists when decisions are made by autonomous AI agents.
AI is confident (and wrong) when the data is bad
An agent given stale or duplicated input produces a confident, well-formatted answer. It does not care if that answer is wrong. Detection lands on whoever received the output. That person is usually a customer, and by that time, the same flawed logic has already run against everything else.
Nobody can prove what an automated decision was based on
Answering "why did an agent do that" needs the record version, the pipeline run, and the transformation behind the output. Lineage for reports is common. Lineage that reconstructs an automated decision across all three is a different requirement. Without one, an engineer spends days rebuilding the trail by hand while a compliance review waits on the answer.
Agents write, and nobody scoped what they can touch
Reading the data wrong produces a bad answer. Writing it wrong changes state. A retry that fires twice, or a bad decision that propagates before anyone sees it, is a different class of failure from a wrong answer on a screen.
Sensitive data that's governed nowhere
The same customer record was copied into a warehouse, a spreadsheet, three integrations, and a backup nobody remembers configuring. Each copy carries the personal data of the original, and none inherited its access rules, so the honest answer to "where does this person's data live and who can reach it" is that nobody knows without a hunt.
End-to-end data engineering services & solutions
We specialize in healthcare data engineering and AI enablement
Every healthcare format supported
Terminology mapping treated as engineering
Patient identity resolved across every source system
Clinical, financial, and operational data reconciled
Clinical notes and documents turned into usable model inputs
PHI boundaries enforced in the data layer, agents included
How we handle sensitive data and audit requirements
HIPAA technical safeguards
GDPR and data minimization
De-identification before data reaches a model
Identity, access, and secrets
Audit logging and lineage
Deployment inside your own cloud account
Our proven data engineering delivery process
Data audit and readiness assessment
We start with a source inventory, profiling against real records rather than documentation, flow mapping between systems, and a quality score per source. You give read access and one domain owner who can settle definition questions. Where a system is undocumented and nobody remembers how it works, profiling reconstructs its behaviour from the records themselves.
Target architecture and data model
We settle the storage choice, batch against streaming, the tenancy model, and the cost envelope each option carries at projected volumes. Your architects review the model and sign off. We document every trade-off, including which assumptions would make a different choice correct, so the decision can be revisited later without being reverse-engineered.
Integration and pipeline engineering
We build the connectors, incremental loads, retries with backoff, validation gates, and alerting that names the source that failed. Your team supplies system access and someone who knows which of two exports is authoritative. We own pipeline reliability, including the upstream systems that change format without telling anyone.
Standardization into one governed layer
This phase covers entity resolution, deduplication, shared definitions, and versioned deployment. Your team confirms what each definition should be, because that is a commercial decision rather than a technical one, and your domain owners are the only people who can make it. We enforce it in code, so a new pipeline cannot introduce a competing version of a metric that already exists.
The AI-ready layer and its evaluation harness
This phase assembles the semantic layer, retrieval, context assembly, and output checks. You name the first automation you want running and supply the known-correct cases the harness scores against, since neither can come from us. We build the layer and the harness that catches outputs that are wrong but plausible before a user sees one.
Handover, then run and optimize
Technologies we work with
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AWS Glue - AWS Lambda
- Amazon S3
- AWS Athena
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Amazon Redshift -
Amazon Kinesis - Amazon RDS
- Amazon ElastiCache
- Amazon SQS
- Amazon API Gateway
- AWS Cognito
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AWS Secrets Manager -
AWS CloudTrail - AWS CloudWatch
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AWS WAF - Azure Synapse
- Azure Data Lake Storage
- Azure Event Hubs
- Azure Functions
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Azure Health Data Services - Apache Iceberg
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Delta Lake -
Apache Parquet - Debezium
- PostgreSQL
- Redis
- TypeScript
- Node.js
- NestJS
- Terraform
- Snyk
- Datadog
- Tableau
- Power BI
Which data architecture
is right for you?
that suits your actual volumes, query patterns, and the first automation you want to run.
Reporting replica of your operational database
Dimensional warehouse on a star schema
Lakehouse on object storage
Streaming and event-driven layer
Canonical model and interoperability layer
Hybrid: raw lakehouse with served marts
Where teams usually go next
Why choose MindK for data engineering
Engineered for the cost curve
We use columnar formats and deliberate partitioning cut storage and compute sharply at volume.
Loads deploy all or nothing
Validation gates run before the served layer is touched, and deployment is versioned.
Multi-tenant by default
Each tenant's processing runs on its own isolated partition with row-level access, so one customer's load never slows another's queries.
AI-ready by design
The same team builds the automation that consumes the layer. Definitions, retrieval scope, and refresh cadence are set against the real workload.
What
our
clients
say
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Allison Erickson
Director of Product, The Lactation Network
USA Such quality work in such efficient timing
«I have nothing but great things to say about our partnership with MindK and the solid work they have done and continue to do for the growth of our company. Our rapport is strong which is a reflection of their professionalism, hard work, and great outputs.»
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Ida Groth
Senior Product Manager, Building Materials Company
Norway Responsibility
and proactiveness«It’s so comforting to know that they see the whole picture and take full responsibility. It’s made all of the difference. I was most impressed with their proactiveness.»
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Per Otto Larsen
Head of CSR Services, CEMAsys.com
Norway High level of detail
and thoughtfulness«The level of detail and thoughtfulness of what they deliver is so good, that a simple explanation of the next idea serves as the basis for them to take it and turn into reality. MindK’s support allows us to focus on core business, product growth and our customers’ needs.»
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Al Hariri
Co-Founder, Vitagene
USA Results-oriented and
outcome-driven«I can tell you confidently that they are different from your regular agency that just wants to charge as much money for their work as they can get away with. MindK is completely results-oriented and outcome-driven.»
Our insigts
Ready to Turn Your Data Into a Competitive Advantage?
Let us know about your technology challenges and we'll
help you resolve them.
FAQ
- What does the audit cover and how long does it take?
A source inventory, profiling against real records rather than documentation, flow mapping between systems, a quality score per source, a target architecture, and a costed roadmap. From you it needs read access to the systems in scope and one domain owner who can settle definition questions when profiling turns up two versions of the same number
- Do we need a warehouse before we can run AI agents?
No. People searching for data science engineering services usually want this question answered, and the sequence depends on the first automation. One that reads a single system needs a reliable pipeline and a scoped retrieval path. One that reconciles across four systems needs identity resolved and definitions governed first, which is closer to warehouse work. The architecture section above covers the trade-offs; the audit argues them against your volumes.
- Can you work on top of our existing stack?
Yes, and that is the usual outcome. Some data engineering service providers arrive with a platform they intend to sell. Replacement here is a finding that has to be justified against your volumes, your cost envelope, and the migration risk.
- Who owns the platform and the code?
You do, both of them. Deployment runs in your cloud account and code lands in your repositories, so the platform we build keeps running on your infrastructure whether or not you keep working with us. The managed cloud services it uses are billed by your provider directly, under your own account, not licensed through us.
- Do you replace our internal data team?
No. Handover and training are explicit phases with their own deliverables: runbooks, documentation, and training run against your own pipelines rather than a generic curriculum. If you have no data team yet, our data engineering consultants can hold the run function until you hire one, then train whoever you hire.
- How do you handle PHI?
De-identification before data reaches a model, access scoped and enforced in the data layer rather than in application code, and an audit trail retained for every pipeline run. The compliance section above lists the controls.
- Our data is genuinely bad. Is it too early to talk?
Bad data is the normal starting point and the reason the audit exists. Profiling gives you the size of the problem in records and engineering hours instead of adjectives, which is what makes it a budget conversation rather than a worry.
- What does an engagement cost?
The audit is priced separately from any build, so the first commitment is small, fixed, and independent of what follows.
- How soon can the first automation run on the new layer?
It depends on how many systems the automation reads and whether identity is already resolved across them, which is usually the long pole rather than the pipeline itself.