Data Engineering for Fintech.
Compliant Pipelines, Auditable from Day One.
SOC 2, PCI, regulatory reporting — data infrastructure that auditors trust.
In fintech, your data infrastructure is not just operational — it is regulatory. SOC 2, PCI, and financial reporting requirements mean every pipeline needs audit trails, every transformation needs lineage, and every data quality check needs to be defensible. Generic data engineering does not account for this. We design the compliance architecture first and build everything else on top of it.
Last updated: June 2026
$12.9M
Average annual cost of poor data quality per organization (Gartner, 2020)
130+
Automated pipelines running across regulated data sources (based on FDE client engagement data)
72h
From development to production for new compliant pipelines (based on FDE client engagement data)
40%
Under budget on a data platform for a 500-person organization (based on FDE client engagement data)
Is This You?
This is for you if
- You need data pipelines that meet SOC 2, PCI, or other financial compliance requirements
- Your data infrastructure was built fast and compliance was added later — now it is fragile
- You need auditable data lineage for regulatory reporting or investor due diligence
- Your analysts are querying production databases because there is no proper data warehouse
- You are consolidating data from payment processors, banking APIs, CRMs, and product databases
Not the right fit if
- You need someone to build trading algorithms or real-time risk models
- Your primary need is product engineering on your core fintech application
- You are looking for a compliance consultant — we are data engineers who build with compliance in mind
- You need 24/7 on-call production database support
What Does Fintech Data Engineering Require?
Financial data carries regulatory weight that most data infrastructure is not built to handle. When auditors ask how a metric was calculated, referencing undocumented legacy scripts is not an acceptable answer.
Compliance Built Into the Architecture
We start every engagement by identifying which regulations apply to your data and building the architecture around those constraints. Data sensitivity classification, role-based access controls, retention and deletion policies, encryption requirements — these are architectural decisions, not afterthoughts. We have built compliant data platforms for regulated industries including healthcare (HIPAA), handling GDPR, CCPA, and LGPD requirements.
Auditable Data Lineage
Every pipeline we build is version-controlled, documented, and traceable. Infrastructure as code means every change is tracked. Automated data quality checks create a continuous audit trail. When a regulator or auditor needs to understand how a number was calculated, the lineage exists from the final report all the way back to the source system.
Data Quality at a Financial Standard
In fintech, a wrong number is not just misleading — it can be a compliance violation. We build automated validation at every stage of the pipeline: completeness checks, range validation, cross-source reconciliation, and business rule enforcement. Issues surface automatically before they reach anyone's dashboard.
Multi-Source Integration at Scale
Fintech companies run on many systems — payment processors, banking partners, CRMs, product databases, and compliance tools. We design reusable pipeline architectures that integrate sources reliably and make adding new ones straightforward. In one engagement, we built 130+ Airflow pipelines across 5 sources, serving 7 data users who depend on accurate, consolidated data every day.
How Does a Fintech Data Engineering Engagement Work?
Compliance and data governance are the starting point, not an add-on. Everything else builds on that foundation.
Compliance and architecture audit
We map your regulatory requirements, classify data sensitivity, and design the architecture to meet SOC 2, PCI, and relevant financial regulations from the start.
Build compliant data infrastructure
Pipelines with audit trails, version-controlled infrastructure, automated quality checks, and role-based access. Built for the standard your auditors expect.
Document and transfer
Data dictionaries, architecture documentation, governance frameworks, and team training. Your compliance team and your data team both understand what was built and how to maintain it. For teams that need continued regulatory monitoring or new pipeline work, we offer maintenance engagements after the initial build.
See our full onboarding process for details on privacy, risk management, and delivery.
Relevant Work Across Regulated Industries
The compliance rigor, data quality standards, and multi-source integration patterns we have built for other regulated industries apply directly to fintech requirements.
Replacing a Legacy ETL Tool with AWS: A SaaS Migration Case Study
Flexible office marketplace SaaS · ~150 employees · Fast-growing, investment-backed · Full Pentaho to AWS migration
6 months
to fully replace Pentaho with a modern AWS stack
4 sources integrated
PostgreSQL, Pipefy, HubSpot, Segment.io
30+ pipelines
running in Airflow
Tech: Apache Airflow · AWS Lambda · AWS S3 · PostgreSQL · Pipefy · HubSpot · Segment.io · Python · SQL · Parquet · Data Modelling
Read case study →
Building a Data Lakehouse from Scratch on AWS: A Case Study for Complex Organizations
Multinational organization · ~500 employees · $32M revenue · No prior data infrastructure
40% under budget
data platform delivered
130+ pipelines
running in Airflow across all sources
72 hours
from development to production for new pipelines
Tech: Apache Spark · Apache Airflow · Apache Iceberg · AWS · Terraform · Docker · PostgreSQL · Salesforce · Freshdesk · Google Analytics · Metabase · Python · SQL
Read case study →
Building Foundational Data Structures
“We've only been working with the FDE team for a short time, but the impact is already becoming clear. Together, we've begun building foundational data structures that will likely support our institution for years to come. The team has been highly communicative throughout the process and has consistently provided thoughtful suggestions and feedback that not only meet our needs, but improve upon them as well.”
Let's Discuss Your Compliance Requirements
Book a free call. We will review your current data setup, discuss the regulatory landscape, and outline what compliant data infrastructure looks like for your company.
Currently accepting 1 of 3 new clients
Frequently Asked Questions About Fintech Data Engineering
Do you have experience with financial data and compliance requirements?
Compliance is where every engagement starts, not where it ends. Our standard process includes data sensitivity classification, access controls, retention policies, and audit trails — the same controls fintech companies need for SOC 2, PCI, and financial regulatory requirements. We have built compliant data platforms for regulated industries including healthcare (HIPAA) and have handled GDPR, CCPA, and LGPD across multiple engagements.
Can you build auditable data pipelines for regulatory reporting?
Yes. Every pipeline we build is version-controlled, documented, and traceable. We use infrastructure as code (Terraform), version-controlled pipeline definitions (Airflow DAGs), and automated data quality checks that create an audit trail. When a regulator or auditor asks how a number was calculated, you can trace it from the report back to the source.
How do you handle data quality for financial data?
Financial data requires a higher bar for accuracy. A single miscalculated metric in a regulatory report can trigger audits, fines, or loss of investor confidence. We build automated data quality checks that run continuously — validating data types, ranges, completeness, and business rules before data reaches reporting. In one engagement we built 130+ pipelines with automated quality monitoring across 5 data sources for an organization with $32M in revenue. The same rigor applies to financial data.
Can you integrate with payment processors, banking APIs, and financial data sources?
We are tool-agnostic and have built integrations with dozens of data sources including PostgreSQL, Salesforce, HubSpot, and various APIs. If your payment processor, banking partner, or financial platform has an API or data export, we can build a reliable pipeline to bring that data into your warehouse. The pattern is the same — we design reusable pipeline architectures that make adding new sources straightforward.
What if we already have some data infrastructure but it is not compliant?
We start with an audit of your existing setup — what exists, what works, what does not meet compliance requirements. From there we either retrofit compliance controls into the existing architecture or rebuild the parts that need it. We do not rip and replace unless the current setup is genuinely unfixable.
How do you ensure data governance across multiple teams?
Data governance starts with documentation and access controls. We build data dictionaries so every team understands what each metric means, set up role-based access controls, establish naming conventions, and create runbooks for common operations. In one engagement, we built a data dictionary that enabled 7 data users, including non-technical ones, to work with complex multi-source data independently.