Financial Data Platforms
Built for Scale and Trust.
We build financial data platforms on Scala and the JVM where data quality, lineage, and governance are built into the architecture. Spark pipelines, data lake infrastructure, and streaming systems for fintech companies that need reliable data at scale.
Spark.
Native Scala
100%
Data lineage tracked
JVM.
Production grade
FP.
Type-safe pipelines
Market feeds, payment events, and transaction streams ingested via Kafka and FS2. Schema validation and source tracking applied at the boundary before data moves downstream.
Distributed Spark jobs transform raw financial data with type-safe schema enforcement. Cats Effect handles side effects during enrichment so transformation logic stays pure and testable.
Delta Lake and Apache Iceberg provide ACID transactions, time travel, and schema evolution on the data lake. Every write is auditable. Historical queries reproduce exactly for regulatory review.
Type-safe query APIs built with Doobie and Tapir serve financial data to downstream consumers. ZIO handles concurrency so high query volumes don't compromise latency or consistency.
Risk models, P&L attribution, regulatory reports, and compliance outputs computed on Spark with full lineage back to source data. Every number is reproducible and auditable.
Why Scala for financial data platforms
Financial data platforms need a stack that
handles scale without losing correctness.
What we build
Scala financial data platform capabilities.
01
Data lake and lakehouse architecture
Delta Lake and Iceberg on Scala and Spark.
We design and build financial data lake and lakehouse architectures using Delta Lake and Apache Iceberg on Scala and Spark. ACID transactions, schema evolution, and time-travel queries built in from day one. Every dataset is auditable, every historical state is reproducible.
02
Spark ETL and batch pipelines
Large-scale financial data transformation on Spark.
We build Spark ETL jobs that process transaction data, market feeds, and financial records at scale. Type-safe transformations, schema validation, and data quality checks built into every pipeline so bad data surfaces early rather than corrupting downstream reports.
03
Real-time streaming data infrastructure
Kafka and FS2 pipelines for live financial data.
We build real-time streaming infrastructure for market data feeds, payment event streams, and fraud detection pipelines using Kafka and FS2. Backpressure, exactly-once semantics, and schema enforcement built into the stream processing layer.
04
Financial reporting and analytics systems
Regulatory reporting and P&L systems on Spark SQL.
We build the reporting and analytics layers that financial platforms depend on for regulatory submissions, P&L attribution, and management reporting. Every calculation traces back to source data, every report is reproducible, and every output meets the standard that compliance teams require.
05
Data quality and governance infrastructure
Data lineage, quality monitoring, and SLA enforcement.
We build data quality monitoring, lineage tracking, and SLA enforcement systems that give your data platform operational visibility. You know when data arrives late, when schemas drift, and when quality checks fail before those problems reach your downstream consumers.
06
ML feature pipelines and data infrastructure
The data foundation that financial ML systems depend on.
We build the feature pipelines, training data infrastructure, and model serving data layers that financial ML systems depend on. Built on Scala and Spark with the type safety and reproducibility that production ML in regulated environments requires.
Built for financial data
The four properties every financial
data platform must guarantee.
01
Lineage
Every piece of data traces back to its source. Every transformation is recorded. Regulators, auditors, and your own engineers can follow any number back to where it came from.
02
Reproducibility
Historical queries return the same results every time. Time-travel on Delta Lake and Iceberg means your platform's state at any point in time is queryable and auditable.
03
Data quality
Schema validation, null checks, and data quality assertions run at ingestion and transformation stages. Bad data surfaces immediately rather than propagating silently through the platform.
04
Scale
Spark on the JVM handles the data volumes that active financial platforms generate without compromising the correctness guarantees that regulatory compliance requires.
Common questions
Questions we get before the first call.
If your question isn't here, it takes one conversation to answer it.
Talk to us →Do your engineers have experience building financial data platforms specifically?
Yes. We match clients with engineers who have built financial data infrastructure in production: Spark pipelines processing transaction data, data lakes with regulatory audit requirements, and real-time streaming systems for market data. General data engineering experience is a baseline, not the qualification.
Can you work with our existing data infrastructure and tools?
Yes. Whether you're already running Spark on Databricks, AWS EMR, or on-premises, and whether you're using Delta Lake, Iceberg, or a traditional data warehouse, our engineers work within your existing infrastructure and extend it. We don't require you to adopt a specific platform.
Our data platform has regulatory requirements. How do you approach compliance?
We build data lineage and audit trail infrastructure into the platform architecture from the start, not as an afterthought. ACID transactions on Delta Lake, schema enforcement at ingestion, and reproducible historical queries are standard in the systems we build. We've worked with teams that have PCI DSS, MiFID II, and SEC reporting requirements.
We have a legacy data pipeline that needs modernizing. Where do you start?
We start by understanding what the pipeline does, where it fails, and what it needs to do that it currently can't. Then we modernize incrementally, starting with the highest-risk or highest-value components. We don't recommend rebuilding everything at once unless the existing system is truly beyond repair.
How quickly can your engineers be contributing?
Most engineers are contributing to active work within days of the engagement starting. They come in knowing Scala, Spark, and the data ecosystem and start with real work from the beginning. We don't do multi-week onboarding periods.