Nearshore big data development services

Senior LATAM engineers who build platforms for data at the volume and speed that breaks a normal database, working your hours, at 30-50% below US rates.

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18+

years delivering software
150+

senior engineers across LATAM
6-8h

daily overlap with US teams
6+

Fortune 500 & global brands trust us

Companies come to us when the nightly job stopped finishing, when the database that ran the business for a decade started timing out, or when a new data source arrives faster than anything can read it.

FusionHit brings senior engineers across Latin America who work out what the real constraint is and build for it, inside your team and on your hours.

Nearshore software development team working at FusionHit's Costa Rica office

Big data consulting

An assessment of your volumes, your growth and what your queries actually have to do, ending in an architecture and a cost projection rather than a recommendation to buy something.

Data platform engineering

The platform built and put into production: ingestion, storage in open table formats, processing, and the orchestration that keeps it running without a person watching.

Batch processing at scale

Spark jobs written to finish inside the window they have, partitioned and tuned so the cost does not climb every month as the data grows.

Streaming and real-time processing

Kafka and Flink pipelines for the data that has to be acted on as it arrives, with the exactly-once and late-arrival handling that decides whether the numbers are right.

Data lake and lakehouse

Raw data organised so it stays queryable and governed, in open formats you can leave, rather than accumulating in object storage until nobody trusts it.

Platform optimization

Existing platforms audited when the bill or the runtime got out of hand: partitioning, file sizes, cluster sizing and the jobs that nobody has needed for a year.

QUALIFICATION

Whether your data is actually big

Every provider on this search will build you a big data platform. None of them will tell you whether you need one, so here is how we work it out on the first call.

The test is not volume alone

A hundred terabytes that get queried twice a month is a storage problem. Two terabytes arriving every hour and being read continuously is not. What decides it is arrival rate, how much of the data is unstructured, and whether anything has to happen to it on the way in.

What it costs to keep running

A big data platform is expensive after it is built, not just to build. Clusters, storage tiers and the engineers who understand them are a permanent line item, and if the workload does not justify it, that line item is the whole return on the project.

When the answer is a warehouse, we say so

Plenty of what gets called big data is a modelling problem wearing a scale costume, and a properly designed warehouse solves it for a fraction of the money. When that is what we find, that is what we tell you, and we build that instead.

Why FusionHit

Why US companies choose FusionHit for big data work

Senior engineering talent

Vetted nearshore engineers with production experience across industries and modern stacks.
Real-time collaboration

Nearshore teams aligned with US time zones for synchronous planning, reviews, and delivery.
Flexible engagement

Scale engineering capacity up or down as your roadmap and priorities evolve.
Integrated partnership

We work as an extension of your engineering and product teams, not as a detached external vendor.

FusionHit senior nearshore engineers working with a US client team

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Fintech

Transaction streams processed for fraud and risk while the decision still matters, with the audit trail retained.

Healthcare

Clinical, imaging and device data at volume, under HIPAA, with PHI restricted everywhere it does not need to be readable.

SaaS

Product event streams turned into usage, billing and retention data that agrees with itself across every tenant.

Logistics

Telemetry from vehicles, devices and facilities processed at the speed dispatch actually decides.

Retail

Transaction, clickstream and inventory data joined across channels, sized for the season rather than the average week.

Manufacturing

Sensor and equipment data from the plant floor kept at a grain that supports prediction rather than only reporting.

Processing engines

Streaming

Open table formats

Platforms

Orchestration & transformation

Cloud

Staff augmentation

Senior data engineers added to your existing team, under your direction, in days rather than months.

Dedicated development teams

A data team working only on your platform, sprint after sprint, while you set the priorities.

Full project outsourcing

You hand over the scope and we deliver it under our management, covering assessment, build, tuning and handover.

How do we know whether we actually need big data tooling?

By what the data does, not how much of it there is. If it arrives faster than a database can absorb it, if a large share of it is unstructured, or if something has to happen to it in flight, the tooling earns its cost. If it is large but tabular and queried on a schedule, a warehouse will be cheaper and simpler. We give you that answer in the assessment, including when it means less work for us.

How much does big data development cost?

Senior nearshore rates run 30-50% below equivalent US salaries and consultancy fees. What a platform costs depends on how many sources feed it, whether any of it has to be real time, and how much of the existing estate has to keep working during the build. You get a line-item estimate on the first call, separate from the infrastructure bill.

What will the platform cost to run every month?

More than most teams expect, which is why we project it during the assessment rather than after go-live. Compute is the largest part and it is driven by how the data is partitioned and how the jobs are written, so the run cost is mostly decided at design time. We report it monthly alongside delivery.

Do we need real time, or is a nightly batch enough?

Most reporting does not need real time and paying for it is a common and expensive mistake. Real time earns its cost when a decision happens inside the window: fraud checks, dispatch, inventory, alerting. For everything else, a batch that finishes reliably beats a stream that nobody reads until morning.

Can you work with the platform we already have?

Yes, and it is a common way we start. We map what is actually running and what it costs, find the jobs and datasets nothing depends on any more, and fix the partitioning and file sizes that are usually the reason the bill grew. Rebuilding is a last resort, not an opening position.

Which technologies do you build with?

Spark for batch, usually on Databricks or EMR. Kafka or Flink for streams. Delta Lake or Iceberg for storage, so the data stays readable by whatever you use next. Airflow for orchestration and dbt for transformation where it fits. We staff for the stack you already run.

What about Hadoop?

We work with existing Hadoop estates, and we do not recommend new ones. Most of what Hadoop was built for is now done more cheaply by object storage plus a query engine, and the operational cost of a cluster is hard to justify for a new build. If you have one, the usual work is migrating off it in stages.

How do you keep a data lake from becoming a swamp?

Open table formats rather than loose files, a catalog that is populated as part of the pipeline rather than as a later project, and ownership assigned per dataset. A lake goes wrong through neglect rather than through design, so the answer is operational rather than architectural.

How do you handle sensitive data at this scale?

Masking or tokenization applied on ingestion where the classification requires it, encryption in transit and at rest, and access controlled per dataset rather than per cluster. For HIPAA, PCI DSS and SOC 2 we keep the evidence as we go rather than reconstructing it at audit time.

Who owns the platform and the code?

You do. Cloud accounts, repositories and infrastructure definitions are yours from the first commit, and the contract assigns all intellectual property to you. We build on open formats specifically so nothing traps you, including with us.

How fast can data engineers start?

We present matched profiles within days, you interview and approve them, and most engineers onboard in under 2 weeks.

How much time zone overlap will we have?

Our engineers work across Latin America on US Central and Eastern time, which gives 6-8 hours of daily overlap. It matters here because a job that fails at 3am is triaged the same morning rather than the next day.

Ready to find out what you actually need?

Tell us what your data looks like and what stopped working, and we will tell you whether this is a big data problem before we quote you for one.

Trusted by leading teams

Rated by our clients

Nearshore big data development services

Senior engineers who build platforms at the scale that breaks the usual tools

Big data services we deliver

Big data consulting

Data platform engineering

Batch processing at scale

Streaming and real-time processing

Data lake and lakehouse

Platform optimization

Whether your data is actually big

The test is not volume alone

What it costs to keep running

When the answer is a warehouse, we say so

Why US companies choose FusionHit for big data work

Senior engineering talent

Real-time collaboration

Flexible engagement

Integrated partnership

Platforms we built and what they made possible

Modernization at Scale in a Distributed Commerce Platform

Making 130,000 Financial Documents Searchable with AI

AI-Augmented Development and Operations in a Multi-Team Enterprise

Your data can stop being the bottleneck.

Where the volume actually comes from