We’ve delivered dozens of enterprise-grade data warehouse projects for leading brands. Our 4,000-strong bench of full-time engineers means you can onboard vetted talent in 2-4 weeks.
No matter what you’re building, we can help.
We’ve been providing data warehouse development services for over a decade. Whether you’re replacing spreadsheet reporting or looking to upgrade an existing enterprise data warehouse, we have the expertise and scale to support your initiatives.
We build custom warehouses from scratch for clients of all sizes. Thanks to our experience, we can support you at every stage of development, from settling definitions and basic design decisions to enterprise implementation.
Our engineers create the data model and pipelines based on agreed-upon definitions, shipping each layer with automated data quality tests designed to cover schema changes and data freshness. Some of our engagements have lasted for years, so the same team that built your warehouse can stay on for long-term maintenance and further development.
We build custom warehouses from scratch for clients of all sizes. Thanks to our experience, we can support you at every stage of development, from settling definitions and basic design decisions to enterprise implementation.
Our engineers create the data model and pipelines based on agreed-upon definitions, shipping each layer with automated data quality tests designed to cover schema changes and data freshness. Some of our engagements have lasted for years, so the same team that built your warehouse can stay on for long-term maintenance and further development.
We staff dozens of experienced data warehouse consultants who can help you make the right choices before you commit to a complex build. Our experts examine your workload, discuss the goals of the build with your engineering leaders and other stakeholders, and help you decide what the platform will eventually look like and how much it will cost.
These difficult questions are settled in live sessions with your team, where we map the internal resources you have, the goals, and the data you’ll be working with. The resulting roadmap sets clear priorities based on your input and known pain points.
Architectural choices made early on can make or break a data warehouse project. Batch or streaming? A single platform or a lakehouse split? We help you choose the right direction that will stand the test of time and won’t inflate your query costs once you scale.
The designs we deliver are modeled against your query patterns and data volumes, allowing you to size storage and compute requirements. The decisions we make together are documented, enabling your in-house team or another vendor to take over once our work is done.
The cloud speeds up implementation, but the cost of getting something wrong can escalate fast. Compute that autoscales without proper controls, with permissive defaults, can “autoscale” your bill, too.
This is where our enterprise experience comes into play. We’ve implemented hundreds of large-scale projects on the big three cloud platforms, and we know what to focus on. Our teams implement no-nonsense access controls and cost guardrails proven across hundreds of accounts. You get a governed platform with excellent documentation, so you can scale without worrying about surprises on your invoice.
Many clients contract us to migrate or modernize legacy platforms, and notable engagements include large-scale migrations for Associated Press and Kinesso. We’ve also modernized data warehouses for clients in regulated industries.
Our migration specialists run the legacy systems and new warehouses in parallel. Everything is validated before the legacy solution is retired. The cutover is phased and approved by the client. Reporting never goes dark, and disruptions are kept to a minimum.
If Finance and Sales report different figures at the end of the month, chances are you won’t find the fault in broken code. In our experience, it’s usually a data integration failure: you have two systems adding up different things under the same name, and not talking to each other.
Our experts deliver consistency, so there are no ambiguous definitions, and each team uses the same models. Your engineers and analysts can trust the numbers, and they can also rely on integrated data from third-party applications and services.
Trust in a number and a definition isn’t a given. It’s the result of testing and planning, catching stale data and inaccurate inputs before they propagate and mess up your reporting.
Our specialists check data quality, test freshness and schema changes, and wire RBAC into the warehouse before your users start seeing numbers that don’t add up. It’s not just the BI layer and your team: auditors get lineage, and your execs get peace of mind.
Spikes in data volumes have a nasty habit of exposing shortcuts taken at the design stage. It’s not uncommon for platforms that launched with blistering performance to slow down to a crawl after two years of heavy use and rising volumes.
For production warehouses, the fix is rarely more compute. Our performance specialists tune queries and storage against measured workloads and clear the technical debt that drags down performance. Instead of throwing more money at infrastructure and kicking the can down the road, you get more efficiency and savings that compound as you scale.
Data warehouse engagements that ran for years.
We’ve built data warehouses from scratch, and we’ve migrated and modernized legacy platforms for global brands. Some of our engagements ran for years, a testament to our engineering acumen and dependability.
FINANCIAL SERVICES
A major Texas-based lender needed to move data analysis out of Excel and into a sustainable platform. They teamed up with us, and we provided a dedicated team of 10 engineers over a 2.5-year engagement. Our engineers built a new data warehouse and data lake in Snowflake and integrated the legacy system with Abrigo CRM. New Tableau dashboards turned a manual data pull into a live view of the loan pipeline.
Snowflake · Tableau · Abrigo CRM
NONPROFIT
One of the largest nonprofits in the US ran its reporting on an aging Teradata warehouse, which made it difficult for its teams to reach relevant data. We provided five specialists who migrated the system to Amazon Redshift on AWS. Performance went up, while the cost went down due to streamlined tooling. New data pipelines now allow any team to source reliable data in seconds.
AWS · Amazon Redshift · Teradata
MARKETING & ADVERTISING
Kinesso, IPG’s performance marketing agency, needed insights from data scattered across various ad platforms, plus a long-term partner to keep it running. Seven of our senior data engineers built the new marketing intelligence engine over a six-year engagement, including a custom ETL tool in SQL that streamlined the migration to Snowflake. We also created a Tableau system that lets Kinesso’s clients pull data without waiting on report analysts.
E-LEARNING & TRAINING | 2.5-Year Engagement | 7 Senior Engineers
UCR needed a new data model to track student performance and dropout rates. We built the data warehouse behind it, using Google BigQuery. The project included four data marts and four fact tables, along with various dashboards used to segment students by cohorts and demographics. The system ended up with more than 800 data quality tests, ranging from data freshness to anomaly checks.
• Delivered data marts, dashboards, fact tables
• Performed 800+ automated data quality tests
• Integrated Looker and dbt for self-serve analytics
Meet the data warehouse experts behind our best work.
Our data engineers and architects average 8-10+ years of experience. They work your hours, so priorities and definitions are aligned with the people who own them in real time. Onboard a data warehouse team in weeks, not months.
Federico S.
Buenos Aires, AR
Manuel C.
Buga, CO
Lucas G.
Rio de Janeiro, BR
Bruno S.
São Paulo, BR
Ricardo A.
Buenos Aires, AR
André P.
São Paulo, BR
Gustavo A.
Marília, BR
Javier V.
Quito, EC
Yuri B.
Blumenau, BR
Diego N.
João Pessoa, BR
Matheus C.
Rio de Janeiro, BR
1,480+
4,000+
96%
500+
Why work with BairesDev? We're trusted by the world's top tech teams.
BairesDev is a US-based company powered by LATAM dev teams. Since 2009, we've built software for companies of all sizes, from scrappy startups to Fortune 500 giants. In fact, we're one of the fastest-growing software development companies in the world. If you're looking for onshore quality with nearshore benefits, we're the partner for you.
Here's what makes us easy to work with and hard to replace:
From code quality to compliance, we've optimized every part of how we work to support high-performing engineering teams. For you, that means clearer communication and smoother delivery, with a partner your team can rely on.
We hire the top 1% of 2.5 million applicants, so you only work with the best.
We give you engineers who've already proven they can deliver. Our rigorous evaluation process includes technical tests, English assessments, soft skill screening, problem-solving simulations, and more. Out of 2.5 million applicants who apply yearly, the top 1% get the chance to join our team. That's how you get highly qualified developers who are experts in their fields.
We hire the top 1% of 2.5 million applicants, so you only work with the best.
We give you engineers who've already proven they can deliver. Our rigorous evaluation process includes technical tests, English assessments, soft skill screening, problem-solving simulations, and more. Out of 2.5 million applicants who apply yearly, the top 1% get the chance to join our team. That's how you get highly qualified developers who are experts in their fields.
We work your team's hours, which creates faster feedback loops and fewer blockers.
Our nearshore software engineers share your workday. Whether we're answering Slack messages, joining daily standups, or presenting a demo to stakeholders, we work alongside your team in real time. Questions get answered quickly. Blockers are resolved as they come up. Simple tasks like reviewing a pull request take 20 minutes, not 24 hours.
Our engineers have strong English skills, so communication and documentation are clear.
We assess English proficiency through verbal interviews and written assignments. Our software engineers can explain decisions, write clear documentation, and actively participate in discussions with your team without barriers. Because we're familiar with US business culture, our devs also know how and when to speak up, how to handle feedback, and what ownership looks like on a team.
With expertise in 100+ technologies, we have every specialist your project requires.
We cover every stage of the software development lifecycle and every major tech stack. The size and breadth of our engineering bench allow us to tackle complex projects for clients of all sizes. This results in fewer handoffs and a smoother development process, speeding up delivery and allowing you to focus the attention of your engineering leadership elsewhere.
Our bench strength lets you scale engineering teams to meet new demands in weeks.
We’re built for scale and efficiency. With 4,000+ vetted engineers and thousands more in our pipeline, we can spin up new teams or scale existing ones on short notice. We move fast without cutting corners, so partners hit aggressive timelines and skip the usual engineering bottlenecks. Once the project is done, clients can downsize accordingly.
We're easy to onboard because we work the way your legal team expects us to.
Because we're based in the US, we follow the same legal standards your team already trusts. We use master service agreements, statements of work, and other documentation that fit your procurement process. You get clear expectations and accountability from the start. So your legal team can easily green-light the engagement with fewer back-and-forths.
We take data and IP seriously, with full protection baked into every engagement.
We treat your assets with the same care you do. Every engagement begins with a mutual NDA and clear IP ownership terms covering everything from code to designs and documentation. Our secure workflows and confidentiality protocols also protect existing codebases and other proprietary data.
Our devs follow security protocols that meet even the strictest enterprise standards. Our software engineers slot into your ecosystem without adding risk. They're trained on secure development practices and only work within the systems and environments they need. Our security protocols include strict access control, audit-friendly workflows, security repository storage, and more. It's how we bring enterprise-grade protection to every engagement, no matter your company size.
We actively track our devs' work to make sure they consistently meet expectations.
With us, you never have to chase updates or second-guess performance. Our delivery managers continually monitor how our software engineers are working to safeguard delivery and quality. They check in regularly, track progress, and even help resolve blockers. It's one more way we streamline projects and accelerate your roadmap.
Need data warehouse expertise? Plug us in where you need us most.
We customize every engagement to fit your workflow and delivery needs. Kick off data warehouse projects in 2-4 weeks.
Production-ready developers
Get senior, production-ready developers who integrate directly into your internal team.
Your hours, your workflows
They work your hours, join your standups, and follow your workflows, just like any full-time engineer.
ALSO AVAILABLE
Spin up focused, delivery-ready pods to handle full builds or workstreams. Together we align on priorities. Then our tech PMs lead the team and drive delivery to maintain velocity and consistency.
Hand off the full project lifecycle, from planning to deployment. You define the outcomes. We take full ownership of the execution and keep you looped in every step of the way.
Build with data warehouse engineers who have experience in your industry.
Every industry argues about a different number, whether that’s a patient visit or a parcel delivery. Our engineers have modeled definitions in enough sectors to know where the edge cases hide.
Provide all teams with one governed set of numbers regulators can easily trace.
Build solutions for:
• Regulatory reporting
• Credit risk aggregation
• Fraud analytics stores
• Customer banking views
• Basel or CECL compliance datasets
• Trading data hubs
• Portfolio dashboards
• AML investigation workbenches
• Branch and channel profitability models
• Open banking data exchange
Provide all teams with one governed set of numbers regulators can easily trace.
Build solutions for:
• Regulatory reporting
• Credit risk aggregation
• Fraud analytics stores
• Customer banking views
• Basel or CECL compliance datasets
• Trading data hubs
• Portfolio dashboards
• AML investigation workbenches
• Branch and channel profitability models
• Open banking data exchange
Clinical and claims data maintained under strict HIPAA access controls.
Build solutions for:
• Claims data warehouses
• Patient outcome analytics marts
• HIPAA-governed access layers
• Clinical trial data repositories
• Health dashboards
• Payer-provider reconciliation
• HL7 and FHIR ingestion flows
• Readmission risk reporting models
• Pharmacy datasets
• Revenue cycle analytics
Inventory and customer behavior stored in a single warehouse that keeps up with seasonal spikes.
Build solutions for:
• Inventory and demand forecasting
• Customer lifetime value models
• Omnichannel sales dashboards
• Promotion effectiveness datasets
• Supplier scorecard platforms
• Basket and affinity analysis stores
• Returns reporting hubs
• Loyalty program data pipelines
• Pricing elasticity workbenches
• Store operations analytics
Bring plant floor and supply chain data together for analyses that nobody needs to reconcile by hand.
Build solutions for:
• OEE and downtime dashboards
• Plant telemetry pipelines
• Yield and scrap analysis
• Supplier quality scorecards
• Bill-of-materials cost models
• Predictive maintenance datasets
• Production scheduling data hubs
• Energy consumption reporting
• Warranty claims analytics
• Digital twin data
Accurate model policy and claims history allow actuaries to query years of data in seconds.
Build solutions for:
• Actuarial modeling datasets
• Claims lifecycle dashboards
• Policy administration data marts
• Underwriting risk scorecards
• Catastrophe exposure models
• Fraud detection analytics
• Reinsurance reporting
• Telematics pricing pipelines
• Agent and broker performance views
• Solvency and IFRS 17 reporting
Measure on-time performance against a single definition every carrier and location reports into.
Build solutions for:
• On-time delivery scorecards
• Fleet telematics data pipelines
• Route cost analysis
• Carrier performance dashboards
• Warehouse slotting optimization
• Demand and capacity planning models
• Freight audit reconciliation
• Customs and compliance reporting
• Last-mile analytics
• Cold chain monitoring
Turn network and billing events into subscriber analytics that hold up at any volume.
Build solutions for:
• Call detail records
• Network performance dashboards
• Subscriber churn models
• Billing reconciliation pipelines
• Capacity planning datasets
• Customer experience scorecards
• Roaming settlement
• 5G rollout analytics
• Service assurance reporting
• Device and plan profitability
Load forecasting and regulatory reporting handled from a single model of your meter data.
Build solutions for:
• Smart meter data platforms
• Load forecasting models
• Outage analytics dashboards
• Grid sensor pipelines
• Renewable generation datasets
• Rate case reporting marts
• Asset maintenance scorecards
• Emissions compliance
• Trading and settlement
• Customer usage portals
We cover your data warehouse stack (and everything around it).
Provisioning a warehouse is easy and doesn’t take long. But making the numbers trustworthy takes real engineering, and we’ve proved we can staff every layer of that work.
Dimensional Data Modeling
Our engineers create star schemas and data vault models, with slowly changing dimensions carried to Type 2, so history stays queryable. Conformed dimensions span subject areas, and surrogate key management covers bridge tables.
dbt, SqlDBM, erwin Data Modeler
ELT Pipeline Development
We handle change data capture off transactional sources, landing operational data raw before any transformation runs. Incremental models process only what moved since the last run, and late-arriving records backfill without a full rebuild. Everything is version-controlled, so we can roll back with load history intact.
Fivetran, Airbyte, Matillion
Data Quality Engineering
Our teams perform freshness and volume checks on every load. Schema change detection fires before a downstream model reads a renamed column. Anomaly detection runs at table and column grain, with thresholds tuned per table and alerts that name the failing object.
Great Expectations, Soda, Monte Carlo
Warehouse Performance Tuning
Warehouse sizing and auto-suspend settings match the workload that actually runs. We rewrite expensive joins or pre-aggregate them into summary tables. We choose clustering and partition keys from measured query profiles.
Snowflake, Google BigQuery, Amazon Redshift
Data Governance & Access Control
We implement row-level security policies and dynamic column masking, defined in the warehouse layer so every downstream tool inherits them. Data management extends to role hierarchies and tag-based classification for regulated fields. Object-level lineage is exposed for audit and access review.
Collibra, Alation, Immuta
Cloud Warehouse Platforms
We offer implementations on all four major cloud warehouses, including workload isolation across teams and compute sizing. Each platform prices differently, and we configure resource monitors and per-team credit budgets during the build.
Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics
Legacy Warehouse Migration
We handle schema conversion and stored-procedure translation off legacy platforms. This includes work with proprietary SQL dialects and BTEQ scripts that automated converters don’t handle well. Row counts and aggregates are compared between source and target until the two agree at every grain and time slice.
AWS Schema Conversion Tool, Informatica, Teradata
Semantic Layers & BI Enablement
Metric definitions are written once in the semantic layer and served to every business intelligence client. Row-level entitlements carry through to the dashboard, and certified datasets give your analysts a governed starting point. Training is included, so they can extend the layer themselves without a ticket.
Data Lake Integration
We implement open table formats over object storage, queried directly from the warehouse through external tables. So one copy of the data serves both engines. Partition evolution and time travel stay available on the lake side, and the security model spans the boundary instead of stopping at it.
Apache Iceberg, Delta Lake, Databricks, Amazon S3
Orchestration & Observability
We build dependency graphs that rerun exactly the models a failed load invalidated, with backfills scoped to the affected partitions. Task-level run history and column-level lineage stay exposed, meaning an on-call engineer can easily trace a stale table back to the upstream job that produced it.
Apache Airflow, Dagster, Prefect
How we deliver data warehouses that drive business value:
Trust is earned at specific delivery phases, from the parallel run before cutover to the no-drama handover. Our process is designed for full transparency, starting from the first conversation.
1. Discovery
Discovery is based on the reports your business already runs: which sources feed them and which definitions teams dispute.
2. Audit the sources
Every source is cataloged and profiled for volume and quality before design starts.
1. Discovery
Discovery is based on the reports your business already runs: which sources feed them and which definitions teams dispute.
2. Audit the sources
Every source is cataloged and profiled for volume and quality before design starts.
3. Platform and architecture
We weigh query patterns and data volumes against each platform’s cost model. We recommend an architecture you can defend to finance, sized for growth and longevity.
4. Data model design
Modeling sessions settle grain and conformed dimensions with your metric owners, so two departments never ship two different numbers.
5. Environments and CI/CD
We set up separate development and production environments. Every transformation is version-controlled and deployed through automated pipelines.
6. Governance and access control
Access roles and column masking go in before real data lands, along with the lineage and audit trails.
7. Validate and sign off
Unit tests run on each transformation, then integration runs across the full pipeline. Parallel runs against the legacy reports settle any disagreement, and you sign off before cutover.
8. Performance tuning
Query profiles from real-world workloads drive our clustering and materialization choices.
9. Instrument monitoring
Observability lands before go-live, with alerts that report failing loads before your users see a stale dashboard.
10. Go-live
Cut over and hand over happen in phases you approve, with documentation and training delivered ahead of time.
Over 130 awards, accolades, and achievements showcase our quality and commitment to client success.
Inc 5000s America's Fastest-growing Companies for 5 years in a row
Recognized for Enterprise Excellence and IT Innovation 2025
Top Software Developers 2026
Entrepreneur of the Year. Bay Area Winner - Nacho De Marco, founder and CEO 2024
Inc 5000s America's Fastest-growing Companies for 5 years in a row
Recognized for Enterprise Excellence and IT Innovation 2025
Top Software Developers 2026
Entrepreneur of the Year. Bay Area Winner - Nacho De Marco, founder and CEO 2024
Inc 5000s America's Fastest-growing Companies for 5 years in a row
Recognized for Enterprise Excellence and IT Innovation 2025
Top Software Developers 2026
Entrepreneur of the Year. Bay Area Winner - Nacho De Marco, founder and CEO 2024
What clients ask about our data warehouse development services:
There’s no shortage of cost drivers, but the two main ones are the number of data sources you plan to consolidate and the amount of modeling your definitions need. Platform costs are secondary, as they scale separately from the actual build. For a real estimate, schedule a call with our team and walk us through your scope.
It all depends on scope. For UCR, we delivered four data marts, four fact tables, and 800+ quality tests in an initial five-month project. We kept building for two and a half years.
With good planning, you can avoid disruptions. We usually run new and old warehouses in parallel and validate the numbers against each other. So, there’s no change until the replacement is ready and you sign off.
Whoever you want: our specialists or your in-house team. Documentation and training are part of the scope, and if you’re modernizing an existing platform, your in-house team will be ready to take over from us. However, many clients keep us on for optimization and support. Nothing about the build forces them to, though.
Neither wins outright, and it depends on what you want to prioritize. BigQuery usually suits organizations using Google’s cloud platform, and its pricing rewards spiky analytical workloads. Snowflake separates storage and compute, and this makes isolating workloads across teams simpler. Ultimately, the answer depends on your query patterns and your stack.
It depends on what your teams do with data. If all data is turned into reports and dashboards, a well-modeled warehouse is enough. Data lakes make sense when data scientists need raw, unstructured data for machine learning. Also, modern lakehouse formats allow one platform to do both.
The same way we make sure our code is good: with automated tests, and plenty of them. Every load runs freshness and volume checks. We set up alerts, while governed definitions stop a fix in one place from breaking something else. That’s how we maintain data quality without an army of expensive analysts.
Put simply, a database runs your business, whereas a data warehouse studies it. Transactional systems handle fast single-record operations, such as a placed order or an updated balance. A warehouse is a centralized repository organized to analyze data, going back years when necessary. These are two different designs for two (very) different jobs. As companies grow, they realize they need both.
Yes, AI-augmented development is a standard practice. We screen for AI proficiency during hiring and constantly invest in upskilling because LLMs are evolving fast. Your environment dictates the tooling. Our engineers work within your VDI, approved toolchain, or whatever AI suite your team runs on. Whether your team uses Claude Code, Cursor, GitHub Copilot, or something else, tool choice follows your standards.
Yes. We operate from US headquarters under US law and jurisdiction. All contracts, billing, and procurement flow through domestic channels, eliminating international complications.
Discuss solutions and decide team structure.
Tell us more about your needs. We'll discuss the best-fit solutions and team structure based on your success metrics, timeline, budget, and required skill sets.
Onboard your team and get to work.
With project specifications finalized, we select your team. We're able to onboard developers and assemble dedicated teams in 2-4 weeks after signature.
We track performance on an ongoing basis.
We continually monitor our teams' work to make sure they're meeting your quantity and quality of work standards at all times.
Accelerate data warehouse development and delivery with vetted nearshore teams.
