Data Engineering
Data Engineering Services
Design, build and operate the pipelines, platforms and governance that turn scattered source data into trusted, analytics-ready and AI-ready data products.
Build the Data Foundation Your Analytics and AI Depend On
Every dashboard, forecast and AI assistant is only as good as the data underneath it. In most organizations that data lives in dozens of operational systems, spreadsheets, SaaS tools and legacy warehouses, each with its own definitions, refresh cycles and quality problems. Data engineering is the discipline of moving, modeling, testing and governing that data so the people and models that consume it can trust what they see.
Hanoi Technology designs and builds modern data platforms end to end: ingestion and change data capture, lakehouse and warehouse architecture, transformation with version control and tests, orchestration, observability, lineage and access control. We focus on maintainable engineering, not one-off scripts, so your platform keeps working as sources, volumes and questions change.
The Challenge
Why Data Platforms Stop Delivering Value
These are the problems we are most often asked to fix when a data platform has lost the confidence of its users.
Brittle, Undocumented Pipelines
Years of hand-written ETL jobs, stored procedures and cron scripts break whenever a source schema changes. Nobody is sure which jobs feed which reports, so every fix risks breaking something else.
Inconsistent Metrics Across Teams
Finance, sales and operations calculate revenue, active customers or margin differently. Meetings start with arguments about whose number is right instead of decisions about what to do.
Rising Cost Without Rising Value
Cloud warehouse and compute bills grow with every new workload, while poorly partitioned tables, full reloads and idle clusters waste budget that should fund new use cases.
Data Not Ready for AI
Machine learning and generative AI need clean, documented, permission-aware data with known freshness. Platforms built only for monthly reporting rarely meet that bar.
Business Value
What Modern Data Engineering Delivers
One Trusted Version of Key Metrics
Shared, tested models for customers, products, orders and finance give every team the same definitions. Business users stop reconciling spreadsheets and start acting on results.
Fresher Data for Faster Decisions
Incremental loads and change data capture replace nightly full refreshes where freshness matters. Operational teams see what is happening now, not what happened yesterday.
Lower and More Predictable Platform Cost
Right-sized compute, sensible partitioning and workload isolation keep spend under control. Cost dashboards show which pipelines and teams drive consumption.
Faster Delivery of New Data Products
Reusable ingestion patterns, templates and CI/CD let engineers add a new source or model in days. The backlog moves instead of growing.
Compliance and Auditability Built In
Lineage, access policies, masking and retention rules are part of the platform design. Answering an auditor or a data subject request becomes a query, not a project.
A Platform Ready for AI Workloads
Curated, documented and governed data feeds feature pipelines, retrieval-augmented generation and model training. AI teams spend their time on models instead of data wrangling.
Treat Data as a Product, Not a By-Product
The most reliable data platforms are run like software products: with owners, version control, automated tests, service levels and release processes. We bring that engineering discipline to every pipeline we build, so your data platform becomes an asset that compounds in value instead of a cost center that needs constant firefighting.
Our Services
Hanoi Technology’s End-to-End Data Engineering Services
Our data engineering practice covers the full lifecycle, from strategy and architecture to day-to-day operations.
Data Platform Strategy and Architecture
We assess your current sources, tools, skills and costs, then design a target architecture that fits your workloads and cloud choices. The result is a phased roadmap with clear milestones rather than a big-bang rebuild.
- Current-state assessment
- Target reference architecture
- Phased modernization roadmap
Data Ingestion and Integration
We connect databases, SaaS applications, files, APIs and event streams using managed connectors, change data capture or custom pipelines where needed. Every source has monitoring, alerting and schema-change handling.
- Source inventory and connector selection
- CDC and streaming pipelines
- Schema drift handling and alerts
Lakehouse and Warehouse Engineering
We build layered storage and modeling on platforms such as Databricks, Snowflake, BigQuery and Microsoft Fabric. Raw, cleaned and business-ready layers keep data reproducible and easy to audit.
- Bronze, silver and gold layers
- Dimensional and data vault models
- Performance and cost tuning
Transformation, Testing and Orchestration
We write transformations as version-controlled code with automated tests, documentation and CI/CD. Orchestration handles dependencies, retries and backfills so pipelines recover without manual effort.
- dbt or Spark transformation projects
- Data quality tests and contracts
- Airflow, Dagster or native orchestration
Data Governance, Lineage and Security
We implement catalogs, lineage, role-based access, column masking and retention policies. Data owners can see who uses their data and how it flows into reports and models.
- Catalog and business glossary
- End-to-end lineage
- Access policies and PII masking
Managed Data Platform Operations
Once the platform is live, our team can monitor pipelines, handle incidents, tune performance and deliver enhancements under a managed service agreement, with weekly written progress updates.
- Pipeline monitoring and incident response
- Cost and performance reviews
- Continuous enhancement backlog
Explore
Explore Our Data Engineering Services
- Big DataDistributed processing and streaming for high-volume, high-velocity data.Learn more
- Data Warehouse ModernizationMove legacy warehouses to scalable cloud platforms with validated results.Learn more
- Data LakehouseCombine lake flexibility with warehouse reliability on open table formats.Learn more
- Data MigrationPlan, execute and reconcile data moves between systems with minimal downtime.Learn more
- Data IntegrationConnect applications, databases and SaaS tools with batch, CDC and API pipelines.Learn more
- Data Mesh ArchitectureDecentralize data ownership to domains with a shared self-serve platform.Learn more
- Data LineageTrace data from source to report for impact analysis, trust and compliance.Learn more
Our Approach
How We Build Data Platforms That Last
A structured path that delivers usable data early and keeps quality measurable throughout.
- 01
Assess Sources and Priorities
We interview data consumers, inventory sources and existing pipelines, and profile real data to find quality issues early. Together we pick the first use cases that will prove value.
Deliverables: Source and pipeline inventory · Data profiling report · Prioritized use-case backlog
- 02
Design the Target Architecture
We choose storage, compute, ingestion, transformation and governance components based on your workloads, skills and cloud strategy, and document the trade-offs.
Deliverables: Reference architecture · Naming and modeling standards · Security and access model
- 03
Build the First Data Products
We deliver a thin vertical slice from source to dashboard or model, including tests, documentation and monitoring, so stakeholders see working results within weeks.
Deliverables: Production pipelines for priority sources · Curated business models · Data quality checks
- 04
Industrialize and Scale
We add CI/CD, reusable ingestion templates, cost controls and observability, then onboard additional sources and domains in regular sprints with demos.
Deliverables: CI/CD and environment strategy · Observability dashboards · Onboarding playbook
- 05
Operate and Hand Over
We run the platform with your team, transfer knowledge through pairing and runbooks, and either step back or continue as a managed service.
Deliverables: Runbooks and documentation · Training sessions · Managed service or handover plan
Use Cases
Data Engineering Use Cases We Deliver
Typical problems our data engineering teams solve for clients across industries.
Unified Customer 360
Combine CRM, e-commerce, support and billing data into one customer model. Marketing and service teams see every interaction and can segment accurately.
Finance Reporting Automation
Replace manual month-end spreadsheet consolidation with automated pipelines from ERP and banking sources. Finance closes faster and with a clear audit trail.
Real-Time Operational Dashboards
Stream order, inventory or logistics events into near real-time dashboards. Operations managers react to delays and stockouts as they happen.
Feature Pipelines for Machine Learning
Build reproducible feature tables for churn, demand or fraud models. Data scientists train and serve models on the same, consistently computed features.
Knowledge Bases for Generative AI
Ingest, clean and index documents and tickets with permissions and freshness metadata. Retrieval-augmented assistants answer from current, authorized content.
Product and Usage Analytics
Collect application events and join them with subscription and billing data. Product teams measure feature adoption and its impact on retention.
Technology
Technologies We Work With
We select tools for your workloads and team, and we are comfortable across the major cloud data ecosystems.
- Databricks
- Snowflake
- Google BigQuery
- Microsoft Fabric
- Amazon Redshift
- Apache Spark
- Apache Kafka
- dbt
- Apache Airflow
- Dagster
- Fivetran
- Airbyte
- Debezium
- Delta Lake
- Apache Iceberg
- Unity Catalog
Platforms
Deliver Data Engineering on Your Preferred Platform
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- Google CloudBigQuery, Looker and Vertex AI for data-driven organizations.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Logistics
Route optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
Read the Full StoryMore Success Stories
Fintech
+162%monthly active usersAll-in-one digital banking app for a traditional bank
A traditional financial institution wanted to compete with neobanks for mobile-first customers. We replaced a set of separate banking, investing and payment apps with one product that people use every day.
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Ready to Build a Data Platform You Can Trust?
Tell us about your sources, reports and the questions you cannot answer today. We will reply within 24 hours and set up a free consultation to outline a practical first step.
Data Engineering FAQ
What does a typical data engineering engagement include?
Most engagements start with a short assessment of sources, existing pipelines, tools and priority use cases. We then design the target architecture and deliver a first vertical slice, from ingestion to a curated model and dashboard, within a few sprints. After that we scale to more sources and domains. Depending on your needs we work as a project team with fixed scope, a dedicated team that extends your own, or a managed service that operates the platform after go-live.
Which cloud data platform should we choose?
There is no universal answer. Databricks suits organizations with heavy data science, streaming and open-format requirements; Snowflake is strong for SQL-centric analytics and data sharing; BigQuery fits well in Google Cloud estates; Microsoft Fabric appeals to teams invested in Power BI and Azure. We compare options against your workloads, skills, existing contracts and governance needs, and we can run a short proof of concept on your own data before you commit.
How long does it take to see results?
With focused scope, a first production data product, such as a sales or operations model feeding a dashboard, can usually be delivered within six to ten weeks. Full platform modernization runs longer and is delivered in phases, each one releasing usable data. We demo working results every sprint and send weekly written progress updates, so you can judge progress on evidence rather than status reports.
What drives the cost of a data platform project?
The main drivers are the number and complexity of sources, data volumes and freshness requirements, the amount of legacy logic to reverse-engineer, and governance or compliance needs. Ongoing cloud consumption is a separate cost, which we estimate during design and keep in check with workload isolation, incremental processing and monitoring. We provide a scoped estimate after the assessment, broken down by phase.
How do you ensure data quality?
Quality is engineered in, not inspected at the end. We profile sources early, define data contracts with producers, and add automated tests for freshness, completeness, uniqueness, referential integrity and business rules to every pipeline. Failures alert the right owner before users notice. Quality metrics are visible on dashboards, so trust in the data is based on evidence.
How do you handle security and sensitive data?
We apply least-privilege access, separate environments, encryption at rest and in transit, and secrets management from the start. Personal and sensitive fields are classified, masked or tokenized according to your policies, and access is logged. We work inside your cloud accounts and follow your security standards, and we document data flows so your security and legal teams can review them.