Data Engineering Services

Most analytics and machine learning projects that disappoint do so for the same reason: the data underneath them was never engineered. Reports disagree because two teams defined the same metric differently, a model degrades because its training data drifted unnoticed, and nobody can say where a number came from.

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10+Years of Industry Expertise 200+Talented Developers 90%Customer Satisfaction Rate

ISO 9001:2015 Certified ISO 27001:2013 Certified

What Clients Say About Our Data Engineering Services

Independent reviews from Clutch and DesignRush. Every review below is shown in full and links back to its source platform so you can confirm it yourself.

4.9/5.0 12 reviews View all reviews on Clutch

5.0/5.0 6 verified reviews View all reviews on DesignRush

5.0

May 2026

“Their balance between technical expertise and product thinking stood out.”

AgileTech Viet Nam developed an AI-powered analytics platform for an AI visibility and GEO solutions company. The platform featured real-time reporting dashboards and data processing pipelines. AgileTech Viet Nam’s work improved the client’s data processing efficiency and the accuracy of AI-generated insights. The platform was user-friendly and supported the client’s first large-scale deployments. AgileTech Viet Nam’s team was communicative, adaptive, and technically proficient.

Improved data-processing efficiency and AI insight accuracy

5.0

Nov 2024

“We saw huge cost savings and optimized stock levels, so we consider AgileTech a partner in our company transformation.”

AgileTech created a working Supply Chain Management (SCM) system that has helped our supermarket chain simplify operations across inventory management, demand forecasting and logistics. They integrated our existing systems, automated inventory and logistics and gave us robust demand forecasting with AI, we saw huge cost savings and optimized stock levels. And they continued to deliver on quality post launch with regular updates and training sessions, so we consider AgileTech as a partner in our company transformation.

AI-driven demand forecasting across inventory and logistics

5.0

Jun 2025

“Their proactive communication, flexibility, and technical expertise stood out throughout the project.”

AgileTech Viet Nam used React Native to develop an iOS and Android app for a real estate platform. The team integrated the app with the client’s backend and implemented property search features. AgileTech Viet Nam’s high-quality work resulted in a 30% increase in user registrations and over 1,000 app downloads within the first month. The team delivered on time, provided regular updates, and responded to feedback. Their flexibility and proactive communication impressed the client.

30% more sign-ups and 1,000+ downloads in the first month

5.0

May 2026

“They followed a structured Agile approach with clearly defined milestones.”

AgileTech Viet Nam developed a web-based logistics management system for an automotive parts supplier. The team integrated the platform with third-party providers and built a real-time tracking dashboard. AgileTech Viet Nam’s work reduced manual processing by 55%, improved real-time shipment visibility, and increased operational efficiency across multiple departments. The team followed a structured Agile approach, communicated consistently, delivered on time, and was flexible.

Manual processing reduced by 55% with real-time shipment visibility

5.0

May 2026

“They were not just executing tasks but were genuinely invested in the product’s success.”

AgileTech Viet Nam developed an MVP for a digital solutions provider’s marketplace platform. They created the UI/UX design, developed the front- and backend, and integrated payment systems. AgileTech Viet Nam successfully launched the MVP on time, allowing the client to onboard 500 users within two months. The platform was stable with minimal issues. Moreover, the team was collaborative, flexible, quick to adapt to changing priorities, and genuinely invested in the project’s success.

MVP launched on time, 500 users onboarded within two months

5.0

May 2026

“Their ability to handle both high-level architecture and hands-on implementation is rare.”

An IT services company hired AgileTech Viet Nam to re-architect their enterprise SaaS platform. The team conducted a technical audit, designed a new architecture, migrated legacy data, and added API gateways. Thanks to AgileTech Viet Nam’s work, the client saw improvements in system scalability, platform uptime, and deployment time. The team followed the Agile methodology, communicated effectively, and was responsive to the client’s feedback. AgileTech Viet Nam helped the client make better decisions.

Improved scalability, uptime, and deployment time on an enterprise SaaS

5.0

May 2026

“They were flexible in accommodating evolving business needs while still keeping the project on track.”

AgileTech Viet Nam developed a digital platform for a real estate firm. They designed the UI/UX, implemented advanced search and filtering functionalities, integrated dashboards, and automated reporting tools. The platform improved operational efficiency, business performance, lead conversion rates, and customer responsiveness. AgileTech Viet Nam implemented a well-structured project management approach, meeting timelines and responding to feedback with flexibility. Their tailored solutions stood out.

Improved efficiency, business performance, and lead conversion

5.0

Mar 2026

“The team demonstrated strong professionalism, reliability, and a proactive approach throughout the project.”

AgileTech Viet Nam developed a scalable engineering workflow system for an ICT company. The goal was to streamline internal processes, improve cross-team visibility, and enhance coordination. The system significantly improved operational efficiency by reducing manual processes and increasing transparency across teams for the client. AgileTech Viet Nam set clear milestones and provided regular updates to ensure everyone remained in sync. Their professionalism and reliability stood out.

Scalable engineering workflow system with fewer manual processes

5.0

Nov 2024

“The platform has greatly improved our operational efficiency, and we wholeheartedly recommend AgileTech.”

Working with AgileTech has been an amazing experience, especially when it comes to creating a personalized e-learning program for our IT services business. We were pleasantly surprised by how adaptable and scalable the platform developed by the AgileTech Team was. Their proficiency with user-friendly course administration and community participation technologies enabled us to provide a thorough instructional resource catered to our customers’ particular requirements. The platform has greatly improved our operational efficiency, and we are amazed by its capacity to manage heavy traffic with ease and offer comprehensive reporting and analytics. The platform remained successful and user-friendly because of the team’s dedication to continuous support and their quick response to criticism. For any organization looking to create innovative and reliable digital solutions, we wholeheartedly recommend AgileTech as a partner.

Personalized e-learning platform that handles heavy traffic

5.0

Nov 2024

“They used a potent tech stack including PHP, React, and AWS to guarantee perfect scalability and zero downtime.”

AgileTech provided a superb Learning Management System (LMS) that creatively and precisely addressed the intricate needs of our organization. Within a scalable, completely customizable platform, the LMS made it possible to create comprehensive courses, administer student tests, engage the community, and obtain real-time data insights. AgileTech’s modular and agile approach made sure that every component, from the user-friendly course builder to the sophisticated analytics and communication tools, was customized to meet our demands despite the difficulties of developing such a feature-rich system. In order to guarantee perfect scalability and zero downtime, even with high user concurrency, AgileTech used a potent tech stack that included PHP, React, and AWS. Their post-launch assistance, which included frequent training sessions and upgrades, gave our staff the means to fully use the LMS’s potential. This project was a testament to AgileTech’s technical skill, rapid deployment, and dedication, making them an outstanding choice for educational technology development.

Feature-rich LMS with course builder, analytics, and comms

5.0

May 2026

“Their professionalism, responsiveness, and ability to execute efficiently made them feel like an operational partner.”

AgileTech Viet Nam optimized a workflow for a consulting firm. The team improved lead management processes and streamlined internal communication systems across multiple departments. AgileTech Viet Nam’s work improved the client’s operations, customer communication process, and overall workflow efficiency. The team was strategic and reliable. Moreover, AgileTech Viet Nam worked closely with the client to understand their business objectives and recommend solutions.

4.5

Apr 2021

“They are incredible to work with and are truly passionate about technology and our concept.”

AgileTech Viet Nam provides mobile app development services for a social media startup. They are building the app using Node.js and Flutter. With the ongoing partnership, AgileTech Viet Nam’s passion has impressed the client so far. The team is truly into technology, and their concept makes the client feel confident about the project’s continuous success.

4.0

Jun 2019

“I was impressed with the flow of the project.”

AgileTech Viet Nam developed a mobile app from scratch. After helping with scoping and objective formation, AgileTech created design mockups and developed the full iOS and Android solutions. Valuable user data is being gathered in ongoing beta tests, which will soon lead to growth and expansion opportunities. AgileTech Viet Nam provided consistent communication and established a smooth workflow. Issues were raised proactively, allowing fixes to come earlier in the process.

5.0

Nov 2024

“AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store.”

AgileTech excelled in supporting our project from initial mock-ups through to the final delivery on the App Store, incorporating data analytics that we could easily share with our customers. The scope of work included mock-up creation, UI/UX design, interactive prototyping, app development, rigorous testing, and setting up a secure production environment on AWS, culminating in the apps release on both iOS and Android platforms.

5.0

Nov 2024

“Since we launched our new site we’ve seen a significant increase in traffic and sales, which proves their work is effective.”

We’ve had an amazing experience with AgileTech for our eCommerce platform. The team worked with us to build an user friendly online store that met all our business needs. Their eCommerce development expertise was evident from the start as they shared valuable insights on how to maximize sales. The AgileTech team are not only talented but also care about their clients success. Since we launched our new site we’ve seen a significant increase in traffic and sales which proves their work is effective.

5.0

Nov 2024

“Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly.”

Working with AgileTech has been a game changer for us. We wanted a modern and responsive website and AgileTech delivered more than we expected. The team was super collaborative and involved us in every step of the process. Their attention to detail in web development and UI/UX design has given us a site that looks great and works flawlessly. The AgileTech team members are not only talented professionals but also great communicators, so we could share our vision and ideas easily. They were so committed to our success and the end result has increased our user engagement and satisfaction so much.

Explore all 16 client testimonials

Benefits of Data Engineering Services

Explore the benefits AgileTech Vietnam delivers for every data engineering services engagement.

One Definition of Each Metric

When finance, operations, and the product team each compute revenue from a different source, meetings are spent reconciling numbers instead of acting on them. A modelled layer with one agreed definition per metric, documented and enforced in code, removes that argument permanently and is usually the single highest-value outcome of the work.

Data Quality Enforced, Not Assumed

Pipelines fail quietly far more often than they fail loudly. Tests on row counts, null rates, referential integrity, freshness, and business rules run with every load, so a broken upstream feed raises an alert before it reaches a dashboard or a model. The alternative is discovering it from a decision that was made on wrong figures.

A Foundation That Analytics and ML Can Build On

Analytics, business intelligence, machine learning, and generative AI retrieval all draw from the same underlying data. Engineering that layer once, properly, means each of those can be built on a source that is already reliable, documented, and governed, rather than each project rebuilding its own extract and adding another version of the truth.

Our Data Engineering Process

Six phases from source discovery to an operating platform, with the definitions agreed before the pipelines are written.

Source Discovery and Profiling

We inventory the systems holding relevant data and profile what is actually in them: completeness, cardinality, duplication, encoding inconsistencies, and the gap between what a field is named and what it contains. This phase routinely changes the plan, because source data is almost always worse than the documentation suggests.

Modelling and Definition Agreement

We work with the people who use each metric to agree exactly one definition per measure and dimension, and write it down. Doing this before building is what prevents the pipeline from encoding one team's assumptions and quietly contradicting another's.

Architecture and Platform Selection

Warehouse or lakehouse, batch or streaming, managed or self-hosted, and the orchestration and transformation tooling are chosen against your volumes, latency requirements, existing cloud commitments, and the skills of the team who will run it after us. We state what each choice costs as well as what it enables.

Pipeline Development

Ingestion, transformation, and loading are built as tested, version-controlled code rather than as configuration in a scheduler that nobody can review. Pipelines are made idempotent and safely re-runnable, because reprocessing after a source correction is a routine event, not an exception.

Quality, Lineage, and Observability

Tests are attached to the pipelines, lineage is captured so any figure can be traced back to its sources, and freshness and volume are monitored with alerting. This is built alongside the pipelines rather than afterwards, since retrofitting lineage to a platform already in use is considerably harder.

Handover and Operation

Documentation, runbooks, and the reasoning behind the modelling decisions are handed over, and your team is brought up to speed on extending the platform. We can continue operating it, but the objective is that you are able to.

AgileTech Vietnam: Your Data Engineering Partner

The reasons global companies trust AgileTech Vietnam to deliver this service, from deep expertise to uncompromising quality and security.

Engineering Discipline Applied to Data

Pipelines are treated as software: version controlled, code reviewed, tested, and deployed through CI. A large share of the data platforms we are asked to repair were built as untested scripts in a scheduler, which is why they cannot be changed safely.

Definitions Before Pipelines

We insist on agreeing metric definitions with the people who use them before building. It slows the start and it is the main reason a platform is still trusted a year later, when the alternative is two dashboards disagreeing and nobody able to say which is right.

Platform Choices Made Against Your Constraints

Warehouse, lakehouse, streaming, and orchestration decisions follow from your volumes, latency needs, cloud commitments, and team skills. We are not tied to a vendor, and we will say when a simpler and cheaper option would serve you as well.

Quality and Lineage as Standard

Tests, freshness monitoring, and lineage are part of the build, not a later phase that gets dropped when the schedule tightens. A platform without them will eventually serve a wrong number confidently, which is worse than serving none.

Built to Be Handed Over

Documentation is produced as decisions are made and your engineers are brought into the work. A platform only your supplier can maintain is a liability regardless of how well it performs today.

One Team Through to Analytics and ML

The same practice covers analytics and business intelligence, machine learning, and generative AI. The people who modelled your data are available to the people building on it, which removes the handoff where most context is lost.

10+ years delivering software and data engineering for start-ups, SMEs, and enterprises.
200+ talented developers across engineering, data, cloud, and QA disciplines.
300+ projects completed with 90% customer satisfaction rate.
Refined Quality Management practices verified by ISO 9001:2015
Powerful Security Management practices supported by ISO 27001:2013
Named Top App Developer in Vietnam by Clutch.co

Technologies We Work With

The platforms, orchestration, and transformation tooling we use to move and model data, selected against your volumes and the team who will operate it rather than a preferred stack.

Warehouses & Lakehouse

Snowflake
BigQuery
Redshift
Databricks
PostgreSQL

Orchestration

Apache Airflow
Dagster
Prefect

Transformation

dbt
Apache Spark
Python
SQL

Streaming

Apache Kafka
Kinesis
Pub/Sub

Cloud

AWS
Microsoft Azure
Google Cloud (GCP)
Terraform

Who can Benefit from AgileTech's Data Engineering Services?

Companies Whose Reports Disagree

The usual symptom is that two teams present different figures for the same measure and the meeting turns into a reconciliation exercise. The cause is almost never a calculation error; it is that each team extracted from a different source and applied a different definition, both defensibly.

Companies Preparing for Machine Learning

Machine learning projects fail on data far more often than on modelling. Training sets assembled by hand from extracts cannot be reproduced, features are computed differently in training and in production, and nobody notices when an upstream source changes shape.

Companies Outgrowing Manual Data Movement

Many organisations run on exports, spreadsheets, and a scheduled script written by someone who has since left. It works until volume grows, until a source system changes, or until an auditor asks how a figure was produced.

Best Practices for Choosing your Data Engineering Partner

Six practical steps to evaluate and select the right partner for your project.

Ask how they handle metric definitions

A partner who starts building pipelines before agreeing what each metric means is deferring the hardest conversation until after the code exists. Expect definition workshops with your business users as an early deliverable.

Require pipelines as tested, reviewable code

Ask where the transformation logic lives and how a change is reviewed and deployed. Logic buried in a scheduler interface or a notebook cannot be tested, reviewed, or safely modified by anyone who did not write it.

Check that quality testing is in scope

Ask what tests run with each load and what happens when one fails. A pipeline without quality gates will eventually deliver wrong data silently, which costs more than a pipeline that stops.

Confirm lineage is captured from the start

You will eventually need to trace a figure back to its sources, whether for an audit or an argument. Retrofitting lineage to a platform already in production is far harder than building it in, so ask when it is delivered.

Test their independence from platforms

A partner who recommends the same warehouse and the same tooling regardless of your volumes, latency needs, and existing commitments is selling a practice rather than solving your problem. Ask what would make them recommend something different.

Establish who can maintain it afterwards

Ask what documentation you receive, how your engineers are brought in, and what happens if you stop working with them. A platform only the builder understands is a dependency, not an asset.

Flexible Engagement Models

Choose the engagement model that fits your project size, budget, and timeline. All models include dedicated communication channels and transparent progress tracking.

Frequently Asked Questions

What is data engineering, and how is it different from data analytics?

Data engineering builds the pipelines, models, and stores that make data reliable and available. Data analytics and business intelligence use that foundation to answer questions and present findings. The handoff is the modelled table: we produce it, analytics consumes it. Our data analytics and business intelligence page covers the second half, and most organisations need both, in that order.

Do we need a data warehouse, a data lake, or a lakehouse?

It depends on your data volumes, how much of it is unstructured, your latency requirements, and what your team can operate. A warehouse suits well-structured analytical workloads; a lakehouse suits mixed structured and unstructured data at larger scale. We assess rather than default, and if your volumes do not justify either, we will tell you that a simpler managed database is sufficient.

What does a data engineering project cost and how long does it take?

It is driven by the number and quality of source systems, how much definitional disagreement has to be resolved, and the latency required. Source profiling in the first phase frequently changes the estimate, because source data is usually worse than documented. We give a detailed estimate after profiling rather than a headline figure before it, and we will say if scope should be reduced.

Can you work with our existing data platform?

Yes. Much of this work is improving something that already exists: adding tests and lineage, restructuring a model that has drifted, replacing scripts with orchestrated pipelines, or migrating from a platform that no longer suits. A rebuild is sometimes justified but it is not the default recommendation.

How do you handle data quality problems in the source systems?

We profile sources before building so the problems are known rather than discovered downstream. Some are corrected at the source, some are handled by transformation rules that are documented rather than hidden, and some are surfaced as quality metrics because the correct answer requires a business decision. What we do not do is silently clean data in a way that makes the underlying problem invisible.

Do you support real-time or streaming data?

Yes, where the requirement justifies it. Streaming architectures carry meaningfully higher build and operational cost than batch, so we ask what decision actually depends on sub-hourly freshness. Frequently the honest answer is none, and a scheduled batch pipeline serves the need at a fraction of the cost.

How does this support machine learning and generative AI work?

Both depend on it. Machine learning needs reproducible datasets, consistent feature computation between training and production, and drift monitoring. Generative AI retrieval needs governed, current, access-controlled source content. Our machine learning solutions and generative AI solutions pages cover what is built on top; this page covers the layer they both stand on.

Which technologies do you work with?

Cloud warehouses and lakehouse platforms on AWS, Azure, and Google Cloud, orchestration tooling, transformation frameworks, streaming platforms where required, and the relational and document stores already in your estate. Selection follows from your constraints rather than from a preferred stack, and we explain the trade-off of each choice.

Who owns the platform and the code?

You do. Pipeline code, transformation logic, infrastructure definitions, and documentation are yours, in your repositories, from the start of the engagement. Handover and enablement are part of the work rather than a separate purchase.

How do we get started?

Tell us which decisions your organisation cannot currently make reliably, and which systems hold the relevant data. We usually begin with a focused profiling and definition exercise on one high-value area, because that produces a defensible plan far faster than an estate-wide assessment.

Trusted and Recognized

Independent directories, certification bodies, and award programs that have assessed our work. Each badge links to its source or names the standard behind it, so every claim on this page can be checked at first hand.

Top Software Development Company - GoodFirms

TechBehemoths Awards - Mobile App Development

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Get a free consultation with our team. Share your project requirements and receive a detailed estimate within 48 hours, including team composition, engagement model, timeline, and cost breakdown.

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AgileTech Vietnam colleagues reviewing project plans