Data Engineering Services: What They Include, How to Scope Them and How to Choose a Partner

Published October 23, 2026 · GIGA IT Editorial Team

Short answer: data engineering services cover the design, build and operation of the systems that move, clean, model and govern your data: pipelines, warehouses or lakehouses, data quality checks, access controls and the data feeds your analytics and AI depend on. To buy them well, start from two or three business use cases, inventory the sources they need, agree on freshness and quality targets, ship one production pipeline first and only then scale. Pick the engagement model (project, dedicated team or staff augmentation) based on who will own the platform after go-live.

Related: advanced analytics and data consulting · hire AI engineers · staff augmentation

At GIGA IT we build data platforms with nearshore engineering teams from Latin America that work inside your time zone. If you already know you need help turning scattered data into reliable analytics, see how our advanced analytics and data engineering team works. If your next step is machine learning or generative AI on top of that data, look at how to hire AI engineers who can work with your data team.

What data engineering services include

"Data engineering" covers very different jobs. Before you ask for proposals, decide which of these you are actually buying:

Service	What you get	Signals you need it
Data platform architecture	Target architecture (warehouse, lake or lakehouse), technology choices and a migration path	Every team keeps its own spreadsheets; nobody trusts one number
Ingestion and pipelines (ETL/ELT, streaming)	Automated, monitored pipelines from your ERP, CRM, product databases, files and APIs	Reports depend on manual exports; data arrives late or breaks silently
Data modeling	Documented, tested models that turn raw tables into business entities (customers, orders, assets)	Two dashboards show different revenue for the same month
Data quality and observability	Automated tests, freshness alerts and lineage so issues are caught before users see them	You find data problems when an executive asks about them
Governance, security and access	Ownership, catalog, role-based access and handling of sensitive data	Audits, regulated data or many teams touching the same tables
AI-ready data	Feature pipelines, document ingestion, embeddings and retrieval feeds for ML and generative AI	AI pilots stall because the data is incomplete, unlabeled or hard to reach
Legacy data migration	Moving data and logic off aging databases or on-premises warehouses without breaking reporting	The old system is expensive, slow or only one person understands it

Why data engineering comes before AI

Most companies looking for data engineering in 2026 are really trying to make AI work. The data layer is where those projects succeed or fail. In a 2024 Gartner survey, 63% of organizations said they either do not have, or are unsure if they have, the right data management practices for AI, and Gartner predicts that through 2026 organizations will abandon 60% of AI projects unsupported by AI-ready data. Gartner also listed poor data quality among the main reasons it expected at least 30% of generative AI projects to be abandoned after proof of concept.

The practical takeaway: if you are planning an AI initiative, budget the data work first and treat it as a product, not as a one-off cleanup. AI-ready data needs pipelines for training and for live production feeds, monitoring and clear ownership.

How to scope a data engineering engagement

1. Start from use cases, not tools. Write down two or three decisions or products the data must support (for example, a weekly margin report, a churn model or an internal assistant over your documents).
2. Inventory the sources. List every system involved, who owns it, how you can access it (API, database replica, files) and any sensitive fields.
3. Set service levels. Define how fresh the data must be, acceptable error rates and who gets alerted when something breaks.
4. Decide the architecture with the team that will run it. The best stack is the one your people can operate; avoid designs that only the vendor understands.
5. Ship one production slice first. One source, one model, one dashboard or feature in production teaches more than a long design phase.
6. Plan the handover from day one. Repositories in your accounts, documentation, runbooks and on-call rules should be part of the scope, not an afterthought.

Engagement models: project, dedicated team or augmentation

Model	Best when	Who leads	Main risk
Project with defined scope	A clear deliverable: a migration, a first data platform, a specific pipeline	The provider, against agreed milestones	Knowledge leaves with the vendor if handover is weak
Dedicated data team	Data is a continuous product with a changing backlog	You set priorities; the team plans and delivers	Needs an internal owner who makes decisions quickly
Staff augmentation	You have a data lead and need specific skills or more capacity	Your data lead	Only works if your team can absorb and direct new engineers

Many companies combine them: a project to build the foundation, then a smaller dedicated team or augmented engineers to run and extend it. If you are weighing those options, our guide to hiring a dedicated development team explains how that model works day to day.

How to evaluate a data engineering company

Criterion	What to ask for
Relevant track record	Two or three comparable projects in production and references you can call
The actual team	Interviews with the engineers and the lead who will work on your platform, not only the sales team
Engineering practices	How they version, test and deploy pipelines; how they monitor freshness and quality
Security and data handling	Access model, how they handle personal or regulated data, where data is processed
Ownership and exit	Code, infrastructure-as-code and documentation in your accounts; a clear exit clause
Collaboration	Working-hour overlap, communication cadence and who makes architecture decisions
AI readiness	Experience preparing data for ML or generative AI, not only dashboards

Red flags

• A fixed technology stack recommended before anyone has looked at your sources.
• No automated tests or monitoring in the proposal, only "development".
• Repositories or cloud accounts owned by the provider.
• Promises of AI results without a plan for the data those results depend on.

Where GIGA IT fits

GIGA IT provides nearshore data engineers, analytics engineers and AI engineers from Latin America who work in U.S.-compatible time zones and use AI-assisted workflows in their day-to-day engineering. Since 2008, GIGA IT has worked with more than 50 clients in 15 countries, with more than 300 specialists and more than 1,215 projects delivered, and a 97% client retention rate. Our U.S. entity, GIGA IT LLC, has its legal address in Houston, Texas.

We can take a defined project (for example, a migration off a legacy data infrastructure), set up a dedicated data team, or add specific engineers to your team. For an example of the kind of problem we work on, read the Tenaris case: when legacy data infrastructure limits business evolution.

Planning a data platform or an AI initiative that depends on your data? Tell us about your sources and use cases and we will help you define scope and team before you request proposals.

Frequently asked questions

Sources

• Gartner, Lack of AI-Ready Data Puts AI Projects at Risk (February 26, 2025).
• Gartner, Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025 (July 29, 2024).
• Joe Reis and Matt Housley, Fundamentals of Data Engineering, O'Reilly Media, 2022.