Data Engineering built for Fintech companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Fintech companies, this means navigating PCI DSS, PSD2 while shipping fast.
Who needs Data Engineering for Fintech?
- Meeting evolving compliance requirements across markets
- Integrating with legacy core banking systems
- Building real-time fraud detection at scale
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Software that moves and manages money must be fast, auditable and bulletproof under regulatory scrutiny.
From Kathmandu, our team delivers Data Engineering for Fintech businesses across with real timezone overlap.
Talk to our Fintech Data Engineering specialists this week
Book a free 30-minute discovery callWhy Fintech teams in outgrow their current Data Engineering setup
- Meeting evolving compliance requirements across markets
- Integrating with legacy core banking systems
- Building real-time fraud detection at scale
- Maintaining uptime during peak transaction volume
- Balancing UX simplicity with strict KYC/AML flows
What makes our Data Engineering different for Fintech businesses in
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- PCI DSS
- PSD2
- SOX
- GDPR
- AML/KYC regulations
Proven Results
"We helped a digital lending platform cut loan approval time from days to minutes while staying fully audit-ready."
Read the full case studyBring your Data Engineering idea to a team that ships for Fintech
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple fintech projects addressing challenges like meeting evolving compliance requirements across markets. We helped a digital lending platform cut loan approval time from days to minutes while staying fully audit-ready.
For fintech clients, we pay close attention to PCI DSS, PSD2, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for fintech clients include meeting evolving compliance requirements across markets and integrating with legacy core banking systems. We tailor our discovery process to surface the specific version of these problems your business faces.