Data Engineering Services
Our data engineering services build the pipelines that pull data from your ERP, CRM, SaaS tools, and legacy databases, check it, and deliver it to the warehouse, reports, and AI that depend on it. Pipelines run on a schedule or when data changes, with an alert when a load fails.
Analytics-ready data
Checked on every load
Pipelines
- ERP
- CRM
- Saa
S apps - Files
SOURCES
What Is Data Engineering?
Data engineering is the work of building and running pipelines that collect data from business systems, clean and reshape it, and deliver it where it is used. The destination might be a data warehouse, a dashboard, another application, or an AI model. When the pipelines are sound, reports stay current and nobody moves files by hand.
Analysts and BI teams depend on it even when they never see it. Most of the work is mapping fields, handling late or duplicate records, and making sure a failed load raises an alert instead of a wrong number.
What is data integration?
Data integration combines data from separate systems so it can be used together. Some pipelines copy data into a shared store, such as a warehouse, on a schedule. Others sync records between applications through APIs. Most data engineering work is data integration with checks built in.
SIGNS
Signs Your Pipelines Need Engineering
Many pipelines start as a script someone wrote to get one report out. These are the signs they've outgrown it.
Data moves by export and upload
Someone downloads a file from one system, reformats it, and uploads it to another every day. The process stops when that person is out.
Failed loads go unnoticed
Scheduled tasks on a server run with no monitoring, so a failed job shows up days later as a stale or wrong number in a report.
Every job reloads everything
Pipelines re-extract all the data every night instead of only what changed, so loads take longer as data grows and strain the source systems.
The same record exists three ways
Customers, orders, or accounts are duplicated across the CRM, the ERP, and spreadsheets, and each report picks a different version.
Only one engineer understands it
The pipeline logic is undocumented and lives in one person's head, so every change and every failure waits for them.
A new source takes months
Each new system needs its own one-off script, because there is no shared pattern for adding sources.
OUR APPROACH
How We Build Data Pipelines
We build pipelines that run without manual steps and alert someone when they break, and we keep the parts that already work. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Script-by-script pipelines
- One-off scripts on a single server
- Full extracts every night
- No alert when a load fails
- Logic nobody has documented
Engineered pipelines
Events where they help, files where they work
Sending events beats sending files when several systems need the same data. A nightly file that is stable and already retries on failure can stay.
Incremental loads
Pipelines move only what changed since the last run, so loads stay short as data grows and source systems carry less load.
Checks on every load
Validation rules, duplicate checks, and referential integrity checks run as data loads, and a failed check raises an alert instead of a wrong number.
Documented and owned by you
You own the pipelines and the code. Documentation covers each source, each transformation, and how to add the next one.
INTEGRATION PATTERNS
Choosing a Data Integration Pattern
There are six common data integration patterns, and most companies use several. The right one depends on how fresh the data must be and how many systems use it.
| PATTERN | HOW IT WORKS | WHEN IT FITS |
|---|---|---|
| Batch ETL | Extract, transform, and load data on a schedule, often nightly | Sources change slowly and next-day data is enough |
| ELT | Load raw data first, then transform it inside the warehouse | The warehouse has spare compute and you want raw history kept |
| Incremental load | Move only the records that changed since the last run | Full extracts run too long or strain the source system |
| Event-driven | A new file or record starts the pipeline when it arrives | Data should land soon after it changes, without constant polling |
| API sync | Applications exchange records directly through their APIs | Two systems must stay in step, not only feed reports |
| Streaming | Records flow continuously through a platform such as Kafka or Kinesis | Several systems need the data within seconds |
How we choose the pattern
We look at how fresh each report needs to be, how many systems read the data, and what already runs reliably. Streaming everything adds complexity most companies don't need, so we use it where several consumers need data within seconds. For a regional bank, a daily batch pipeline was the right answer. For a disaster recovery company, event-driven, incremental loads replaced full daily extracts.
WHAT WE BUILD
Data Engineering Work We Do
Our data engineering consulting covers pipelines, integrations, and the checks that keep data correct.
Pipelines into your data warehouse
ETL and ELT pipelines that load ERP, CRM, operational databases, and spreadsheets into a data warehouse. For a post-acute care provider, we unified four separate systems with different data structures.
System-to-system data integration
Pipelines and APIs that move records between applications without re-keying. For a regional bank, an AWS Lambda and Python pipeline moves about 30,000 client records a day from FIS DataConnection to the bank's CRM. See systems integration.
Event-driven pipelines on AWS
Pipelines on Amazon S3 and EventBridge that start when new data arrives. For a disaster recovery company, event-triggered, incremental loads replaced manual daily extracts from Salesforce, Sage, and Excel. See AWS consulting.
Lakehouse pipelines on Microsoft Fabric
Pipelines that move data through Bronze, Silver, and Gold layers, from raw copies to report-ready tables. For an industrial distributor, we built on Fabric and Azure SQL Server, with self-service Power BI reporting.
Data pipelines for AI
Clean, current data for AI-powered analytics and models. For a national residential flooring retailer, we built a scheduled ETL pipeline that loads up to five years of sales and outcome data from four systems into PostgreSQL for an AI coaching engine.
HOW IT WORKS
How a Data Engineering Project Runs
A data engineering project runs in five steps, from assessing sources to running pipelines in production with monitoring.
STEP 1
Assess sources
We inventory your systems, data quality, and the jobs that move data today.
STEP 2
Choose patterns
We pick batch, event-driven, or API integration for each source.
STEP 3
Build and test
We build pipelines with automated cleansing and validation, and test them.
STEP 4
Release in stages
The most important sources go live first, and the rest follow in stages.
STEP 5
Monitor and hand off
We set up alerts and logs, then hand over to your team.
Monitoring and alerts
A pipeline that fails silently is worse than no pipeline, because reports keep showing numbers that look right. We build monitoring into the pipelines, so a failed load or a failed check raises an alert. For the disaster recovery company, monitoring and documentation replaced scheduled tasks that ran with none.
What your team gets at handoff
You own the pipelines and the code. Services we can provide at handoff include knowledge transfer sessions and documentation covering each source, each transformation, and how to add new ones. Ongoing support is also available.
RESULTS
Data Engineering Results
For a regional bank, JBS Dev replaced the manual handling of about 30,000 client records a day with an AWS pipeline, delivered in just under four months. For a national disaster recovery company, JBS Dev replaced manual daily extracts with an event-driven AWS pipeline that became the single source of truth for executive reporting.
30,000
Records a day, with no manual work
Financial Services
Automating 30,000 Daily Records for a Regional Bank
Challenge: Staff moved about 30,000 records a day from FIS DataConnection to ActiveCampaign by hand, after two to three years of failed automation attempts.
Result: An AWS Lambda and Python pipeline now runs the transfer daily with no manual work, delivered in just under four months.
Read the regional bank case study100M+
Records scored for loan risk
Lending
Real-Time Loan Risk Analytics for a Lending Analytics Company
Challenge: Users needed loan performance reports across the market and for each portfolio.
Result: A multi-tenant platform on AWS EMR, Glue, and Redshift processes more than 100 million records, and its machine learning engine scores each loan's risk.
Read the loan analytics case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
ARCHITECTURE
How Pipelines Start When Data Arrives
In an event-driven pipeline, new data starts the load instead of a clock. A file or record lands in storage, a rule detects it, and the pipeline moves only the new data, checks it, and loads it. A failure raises an alert.
We build these on AWS with Amazon S3, EventBridge, AWS Lambda, and AWS Glue, and on Microsoft with Azure Data Factory and Microsoft Fabric.
EVENT-DRIVEN LOADS
New data in Amazon S3
EventBridge rule
Data warehouse
CHECKS AND ALERTS
Validation
Runs on every load
Alerts
A failed load notifies the team
COST AND TIMELINE
What Drives Data Engineering Cost and Timeline
Data engineering cost and timeline depend mostly on the number of sources and how clean and accessible they are. Six factors drive most estimates:
- 01Number and type of data sources
- 02Access to source systems and APIs
- 03Data volume and history
- 04Data quality
- 05How fresh the data must be
- 06Monitoring and support after launch
How the timeline is set
Timelines depend on the number of sources and the state of the data, and the assessment sets them. We release pipelines in stages, starting with the sources behind the most important reports. As a reference point, a regional bank's daily data pipeline went from kickoff to running in just under four months.
How cost is set
We don't quote a number before we understand your sources. The assessment turns the factors above into an estimate for each stage, plus the ongoing cloud cost of running the pipelines. Choosing the simplest pattern that meets the need, such as a nightly batch instead of streaming, keeps both down.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose a Data Engineering Partner
Pipelines run every day for years, so the team that builds them matters. Ask any partner these four questions.
- Will they keep what already works?
- Yes. A nightly file transfer that is stable and retries on failure can stay. We replace the pipelines that fail, run too long, or need manual steps, and leave reliable ones alone.
- How will we know when a load fails?
- We build monitoring and alerts into the pipelines, and logs show what ran and when. A failed load or a failed data check notifies your team before anyone reads a wrong number.
- Who writes the pipelines?
- Senior engineers. Engagements are led by senior engineers and technical leaders who can make architecture, integration, and security decisions directly, with fewer handoffs and faster decisions.
- Where is the team?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work. From the first call, you will know exactly who is building and supporting your pipelines.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Data Engineering Services FAQs
What do data engineering services include?
Data engineering services cover assessing your data sources, designing and building pipelines, adding data quality checks, and running the pipelines with monitoring. Ours include ETL and ELT into data warehouses, system-to-system data integration, event-driven pipelines on AWS, Microsoft Fabric pipelines, and data for AI, with documentation your team can use.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse. ETL suits smaller warehouses and sensitive fields that should be cleaned before they land. ELT suits cloud warehouses with spare compute and teams that want raw history kept. Many pipelines use both.
What is the difference between data engineering and data integration?
Data integration is one part of data engineering. Integration connects systems so their data can be used together, through pipelines or APIs. Data engineering also covers modeling, data quality, monitoring, and running the pipelines over time. A project that starts as one integration usually grows into data engineering.
Do we need real-time data pipelines?
Usually not. Most reports work well with hourly or nightly data, and batch pipelines are simpler to build and run. Event-driven or streaming pipelines make sense when several systems need data soon after it changes. We match the pattern to the need, and a stable nightly file can often stay as it is.
How long does a data engineering project take?
It depends on the number of sources and the state of the data, and the assessment sets the timeline. We release pipelines in stages, so the most important sources go live first. For a regional bank, a daily data pipeline went from kickoff to running in just under four months.
How much do data pipelines cost to build and run?
It depends on the number and type of sources, access to APIs, data volume, data quality, how fresh the data must be, and support after launch. The assessment turns those factors into an estimate for each stage, plus ongoing cloud costs. Simpler patterns, such as nightly batches, usually cost less to run.
Which tools and platforms do you use?
We pick tools for the job. On AWS that means Amazon S3, EventBridge, AWS Lambda, AWS Glue, and Step Functions. On Microsoft, it means Azure Data Factory, Microsoft Fabric, and SSIS. We also work with Databricks, Fivetran, Apache Spark, Kafka, Kinesis, SQL Server, PostgreSQL, Aurora, MySQL, and MongoDB.
Can you fix pipelines that keep breaking?
Yes. We start with an assessment of what runs today and what breaks. We fix the pipelines the business depends on most first, then rebuild ingestion with event-driven loads, monitoring, and documentation where it's needed. Pipelines that already work stay as they are.
