The most common reason analytics initiatives fail is not a lack of tooling or ambition. It is that the data underneath them is not trustworthy. Dashboards built on broken pipelines produce reports that analysts spend hours manually verifying. AI systems trained on fragmented data hallucinate. Operational decisions made on stale numbers cost real money every day they go uncorrected.

Gradion approaches data engineering as infrastructure. The pipelines, schemas, and transformation layers we build are the foundation that determines what your data organization can do for the next five years. The systems we have built or maintain move more than $10 billion in GMV annually. At that scale, data reliability is not a nice-to-have. It is the business.

What We Solve

Your dashboards show different numbers depending on who pulls the report. This is almost always a pipeline and transformation problem. Data arrives from multiple sources with different schemas, naming conventions, and update frequencies. Without a governed transformation layer, every analyst builds their own interpretation of what the numbers mean. We build the single source of truth: ingestion pipelines that bring data in reliably, transformation logic in dbt that is version-controlled and testable, and a warehouse or lakehouse architecture designed for consistent, governed access. When a transformation breaks six months after the engagement ends, your team can read the logic, find the problem, and fix it without calling anyone.

Your operations team cannot see what is happening in real time. Logistics platforms tracking shipment state, marketplaces updating availability, payment systems propagating transaction events - these systems need data flowing in near real time, not overnight batch jobs. We build Kafka-based streaming architectures that scale to millions of events per day. For HomeToGo, the ingestion and normalization layer underpinning real-time availability search across 15 million+ listings and 100+ partner API integrations is not a batch job. It is a continuously updated data platform where a poorly designed schema or a brittle integration brings down availability for millions of searches.

Your data is spread across systems with no single operational view. Four databases, three ERPs, a spreadsheet layer, and an email thread that explains what the numbers actually mean. This is the starting point for most of our engagements. The work is consolidation: mapping what exists, reconciling conflicts, building a central warehouse that gives every team access to the same governed data. The architecture decision - cloud warehouse (Snowflake, BigQuery, Redshift) or open lakehouse (Delta Lake, Apache Iceberg) - depends on your query patterns, latency requirements, and existing infrastructure. We design for where you need to be in three years, not just what solves today's problem.

You are about to build an AI layer and the data underneath it is not ready. AI systems are only as reliable as the data they consume. If the ingestion pipelines are fragile, the schemas are inconsistent, or the data quality is unmonitored, the AI layer will hallucinate, produce errors, and get switched off. Our data readiness assessment - described in detail on the Generative AI Applications page - evaluates whether your data infrastructure can support AI workloads. When it cannot, the data engineering work comes first. This is the most common path into a Gradion AI engagement: fix the data, then build the intelligence layer on top of it.

You have no way of knowing when the data is wrong. Automated data quality checks embedded in pipeline execution catch problems before they reach analysts. We instrument schema drift detection, null rate monitoring, referential integrity checks, and statistical anomaly alerts on key metrics. Pipeline execution logs and SLA tracking ensure engineers know when something is wrong before a business user reports it. This observability layer is built into every engagement, not offered as a separate add-on.

How We Build

Pipeline architecture starts with latency requirements, not tool preference. For batch workflows, Airflow and Prefect handle orchestration reliably. For systems that need sub-minute data freshness, Kafka-based streaming architectures bring event data into the platform as it is generated. The choice is driven by what the business needs, not what we prefer to deploy.

Transformation logic is written to be owned by your team. dbt is our default transformation layer because it is version-controlled, testable, and readable by analysts who do not write application code. Every transformation is documented with its business logic, not just its SQL.

Schema design, partitioning, and access patterns are decided at the start. Not retrofitted when queries start slowing down. The data model is the architectural decision with the longest lifespan - getting it right at the beginning saves months of rework later.

Built to Be Owned by Your Team

Every engagement includes documentation, runbooks, and data contracts. The team that owns the pipeline after Gradion leaves should be able to operate it, extend it, and debug it without external support.

That means documented schemas with agreed definitions for shared metrics. Clear ownership of each pipeline stage. Runbooks written for the actual team - their skill level, their tooling, their operational context. Data contracts between producing and consuming systems so changes do not propagate silently.

This commitment is not a closing deliverable. It is a design constraint that shapes every technical decision during the engagement. If we cannot hand it over cleanly, we have not built it correctly.

Proof in Production

HomeToGo - real-time data platform at marketplace scale. HomeToGo's vacation rental marketplace handles real-time availability search across 15 million+ listings drawn from 100+ partner API integrations, serving 60,000+ partners with 50+ production deployments per day. Gradion built and scaled the data platform across 150 engineers in three countries. The ingestion, normalization, and search infrastructure operates continuously at a scale where pipeline reliability directly determines whether millions of searches return accurate results.

Vietnam’s largest coffee chain - four databases consolidated, 12% revenue growth in three months. Vietnam’s largest coffee chain had four fragmented databases across their 928-outlet Vietnam operation. No single view of performance, no real-time reporting, no ability to measure campaign effectiveness at the store level. Gradion consolidated the data into a central warehouse, built the reporting layer, and unlocked real-time operational and campaign-level insights across every outlet. Revenue grew 12% within three months of rollout.

Senior Aerospace Thailand - operational efficiency from 55% to 95%. Senior Aerospace Thailand had production data spread across systems with no single operational view. Teams could not see production line performance in real time. Gradion built a custom analytics layer integrated directly with their Infor Syteline ERP, giving operational teams real-time visibility across both production lines. Operational efficiency moved from 55% to 95%. The system runs as production infrastructure, not a reporting tool.

When to Build Custom - and When a Managed Service Is Enough

For standard source-to-warehouse ingestion from well-supported SaaS systems, managed services like Fivetran or Stitch often work well enough. If your data sources are standard, your transformation logic is straightforward, and your latency requirements are measured in hours rather than seconds, a managed stack may be the right choice.

Custom data engineering is warranted when your data sources are proprietary or non-standard (custom ERPs, partner APIs with no connector, legacy systems with undocumented schemas). When your latency requirements demand streaming rather than batch. When transformation logic encodes complex business rules that a managed tool cannot express. When data residency, security, or compliance requirements mean the pipeline must run inside your own infrastructure.

The data architecture assessment is designed to answer this question before any build commitment is made. If a managed service is the right answer, we will tell you.

How Data Engineering Connects to Other Gradion Services

Data engineering is often the prerequisite for other work. The relationship is direct:

Generative AI. The data readiness assessment on the GenAI page evaluates whether your data infrastructure can support AI workloads. When it cannot, the data engineering engagement comes first. The two are sequential phases of the same objective - reliable data in, reliable intelligence out.

Legacy modernization. Many legacy systems are also the primary data sources. A legacy migration often includes rebuilding the data layer as part of the platform modernization. The data engineering and migration teams coordinate directly.

Transformation roadmaps. When the roadmap includes a data strategy component - consolidating fragmented systems, building a reporting layer, establishing data governance - the data engineering practice executes that workstream.

Engagement Structure

Data Architecture Assessment 2–3 weeks. We evaluate your current data landscape: sources, pipelines, storage, transformation logic, quality, and the gap between where you are and where you need to be. The output is an architecture recommendation, a prioritized build plan, and a clear assessment of whether custom engineering or managed services best fit your requirements. Scoped as a fixed-fee engagement.

Data Platform Build 3–6 months. Design and implementation of the data infrastructure: ingestion pipelines, warehouse or lakehouse architecture, transformation layer, quality monitoring, and integration with downstream systems. Built in structured phases with working increments - each phase delivers a functional component, not a plan for one. Includes documentation, runbooks, and data contracts for handover. Scoped based on source complexity, latency requirements, and integration scope.

Ongoing Platform Support For organizations that want Gradion to maintain and evolve the data platform after the initial build. This covers pipeline monitoring, incident response, schema evolution, new source integration, and periodic optimization as data volumes and usage patterns change. A named engineer maintains continuity with your architecture. Scoped as a monthly retainer.