Data Engineering — NXC International

CONSULTING SERVICE

Wedesignandoperatethedatainfrastructurethatturnsraw,scattereddataintotrusted,actionableassets,atanyscale.

Delivered

100+ projects

Overview

Our data engineering practice designs and operates the foundational infrastructure that transforms raw, scattered data into trusted, actionable assets your teams can rely on.

We architect end-to-end data ecosystems, from ingestion and transformation to storage, streaming, and observability, whether you are modernizing a legacy data warehouse, building a real-time analytics platform, or establishing a lakehouse architecture on Azure or AWS.

What We Do

Our core offerings include:

Data pipelines and ETL/ELT engineering
Data lakehouse and warehousing
Real-time streaming and analytics
Data quality and observability

Our lakehouse solutions, built on platforms such as Delta Lake, Azure Synapse, Databricks, or Snowflake, eliminate data silos and provide a single source of truth for analytics, machine learning, and reporting.

Our Approach

We build reusable, modular data infrastructure rather than one-off pipelines, so every new data source or use case can be onboarded quickly.

We apply data mesh principles to distribute ownership of data domains while enforcing centralized governance, and we treat data pipelines with the same reliability standards as production software.

Get Started

Speak with our bilingual data engineering experts to assess your current data landscape and map a clear path to a high-performance, future-ready data platform.

WHAT WE OFFER

Four data engineering capabilities

Pipelines & ETL/ELT

Robust pipelines that extract, transform and load validated datasets, with an ELT-first approach.

Pipelines & ETL/ELT

Robust pipelines that extract, transform and load validated datasets, with an ELT-first approach.

Lakehouse & Warehousing

Unified platforms on Delta Lake, Azure Synapse, Databricks or Snowflake that eliminate data silos.

Lakehouse & Warehousing

Unified platforms on Delta Lake, Azure Synapse, Databricks or Snowflake that eliminate data silos.

Real-Time Streaming

Event-driven architectures using Kafka, Event Hubs and Kinesis to analyze data as it is generated.

Real-Time Streaming

Event-driven architectures using Kafka, Event Hubs and Kinesis to analyze data as it is generated.

Data Quality & Observability

Schema validation, anomaly detection, freshness checks and lineage tracking catch issues at the source.

Data Quality & Observability

Schema validation, anomaly detection, freshness checks and lineage tracking catch issues at the source.

Let's talk

OUR APPROACH

Platforms built to scale and last

Three principles keep every data platform trustworthy.

Platform-first architecture

Reusable, modular infrastructure lets new data sources and use cases onboard without rebuilding from scratch.

Platform-first architecture

Reusable, modular infrastructure lets new data sources and use cases onboard without rebuilding from scratch.

Platform-first architecture

Reusable, modular infrastructure lets new data sources and use cases onboard without rebuilding from scratch.

Data mesh ownership

Domain teams own their data products while centralized governance sets standards and discoverability.

Data mesh ownership

Domain teams own their data products while centralized governance sets standards and discoverability.

Data mesh ownership

Domain teams own their data products while centralized governance sets standards and discoverability.

Observability-driven

Pipelines get production-grade SLAs, alerting, lineage tracking and continuous quality monitoring.

Observability-driven

Pipelines get production-grade SLAs, alerting, lineage tracking and continuous quality monitoring.

Observability-driven

Pipelines get production-grade SLAs, alerting, lineage tracking and continuous quality monitoring.

Projects delivered in cloud, security and AI

Certification domains held across our expert team

Cloud platforms mastered

FAQ

Data engineering questions

Our team can help with the next steps

Get started

What does your data engineering practice include?

Data pipelines and ETL/ELT engineering, lakehouse and warehousing, real-time streaming and analytics, and data quality and observability.

Which platforms do you work with?

Our lakehouse solutions are built on Delta Lake, Azure Synapse, Databricks or Snowflake, with streaming on Apache Kafka, Azure Event Hubs and AWS Kinesis, on Azure or AWS.

Can you modernize our legacy data warehouse?

Yes. Modernizing a legacy data warehouse, building a real-time analytics platform and establishing a lakehouse architecture are all part of what we deliver.

How do you ensure data quality?

We implement automated frameworks including schema validation, anomaly detection, freshness checks and lineage tracking, so issues are caught before they propagate downstream.

What is the data mesh approach?

We distribute ownership of data domains to the teams closest to the data, while enforcing centralized governance, standards and discoverability.

What does your data engineering practice include?

Data pipelines and ETL/ELT engineering, lakehouse and warehousing, real-time streaming and analytics, and data quality and observability.

Which platforms do you work with?

Our lakehouse solutions are built on Delta Lake, Azure Synapse, Databricks or Snowflake, with streaming on Apache Kafka, Azure Event Hubs and AWS Kinesis, on Azure or AWS.

Can you modernize our legacy data warehouse?

Yes. Modernizing a legacy data warehouse, building a real-time analytics platform and establishing a lakehouse architecture are all part of what we deliver.

How do you ensure data quality?

We implement automated frameworks including schema validation, anomaly detection, freshness checks and lineage tracking, so issues are caught before they propagate downstream.

What is the data mesh approach?

We distribute ownership of data domains to the teams closest to the data, while enforcing centralized governance, standards and discoverability.

What does your data engineering practice include?

Data pipelines and ETL/ELT engineering, lakehouse and warehousing, real-time streaming and analytics, and data quality and observability.

Which platforms do you work with?

Our lakehouse solutions are built on Delta Lake, Azure Synapse, Databricks or Snowflake, with streaming on Apache Kafka, Azure Event Hubs and AWS Kinesis, on Azure or AWS.

Can you modernize our legacy data warehouse?

Yes. Modernizing a legacy data warehouse, building a real-time analytics platform and establishing a lakehouse architecture are all part of what we deliver.

How do you ensure data quality?

We implement automated frameworks including schema validation, anomaly detection, freshness checks and lineage tracking, so issues are caught before they propagate downstream.

What is the data mesh approach?

We distribute ownership of data domains to the teams closest to the data, while enforcing centralized governance, standards and discoverability.

RESEARCH & INSIGHTS

R&D behind our data work

Explore the research and development projects NXCI is building in-house.

See all R&D

Artificial Intelligence

Data Engineering

Microsoft 365

Artificial Intelligence

Data Engineering

Artificial Intelligence

Data Engineering

OUR SERVICES

More of our services

DevSecOps

Managed CI/CD, automation and Infrastructure as Code with security built into delivery.

DevSecOps

Managed CI/CD, automation and Infrastructure as Code with security built into delivery.

SOC & Threat Modeling

Threat modeling and a 24/7 Security Operations Center working as one feedback loop.

SOC & Threat Modeling

Threat modeling and a 24/7 Security Operations Center working as one feedback loop.

Let's help you succeed

We'd love to learn about your business, understand your objectives, and explore how we can help you achieve it.