Overview

HighView Bank is a US bank offering retail and commercial banking services. The client’s analytics ran on SQL Server, and the ETL load was too heavy to run during business hours without slowing queries, so it ran overnight. Due to this, the fraud and risk teams received transaction data a day late, and transactions posted during the day were not scored until the following morning. On top of this, the data pipelines were written manually with no documentation, so adding a source took two to three weeks, and a reported regulatory figure could not be traced back to the transactions it came from. Bacancy Technology migrated the client’s data warehouse to Snowflake, replaced the batch ETL with dbt-powered ELT and streaming ingestion, and moved fraud and risk models into Snowpark. Reporting latency dropped from a 24-hour delay to continuous, same day reporting, the ETL running costs fell 75%, and every reported figure now traces to its source records.

Technologies Used

Snowflake
dbt
SQL Server
Fivetran
Power BI
Git

Project Highlights

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SQL Server warehouse migrated to Snowflake

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Batch ETL was rebuilt as incremental ELT

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Fraud and risk models moved into Snowpark

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Regulatory reporting rebuilt as dbt models

The Challenges

1

Every system that read from the SQL Server warehouse had to wait on the overnight batch. Fraud detection and risk reporting ran on data that was up to 24 hours old, so the bank was catching any suspicious patterns after the exposure window had already passed.

2

The ETL pipelines were manually coded and undocumented. The original developers who had worked on this setup had left the company, so any change to a transformation required reading through the scripts first, and adding a new source took two to three weeks of custom development.

3

The bank files quarterly reports with its regulators. Those figures could not be traced back to the transactions they came from, so analysts had to manually gather the evidence for each examination and rebuild each calculation by reading the pipeline scripts.

4

The bank was scoring fraud and risk models in a separate environment outside SQL Server. So, the data was getting extracted for every model run, which created copies of customer transaction data and added a delay before scoring.

Solutions by Bacancy

1

Our Snowflake migration team migrated the warehouse from SQL Server to Snowflake, and added streaming ingestion for the transaction feeds, so records land continuously instead of having to run overnight. We allocated a dedicated compute to ingestion, separate from the warehouses serving queries, so it runs through the business day.

2

Bacancy Technology’s dbt developers rebuilt the pipeline logic in dbt. We wrote each transformation as a SQL model, added tests to every model, documented what each one does, and moved the whole project into version control. We replaced the custom ingestion scripts with Fivetran connectors and configured them per source.

3

Our data engineers rebuilt the regulatory reporting datasets as dbt models, one per report, and wrote the calculation for each figure in SQL against the transaction-level records behind it. We repointed the Power BI reports at those models, and applied Snowflake RBAC and masking on customer identifiers across those datasets.

4

The client took help from our Snowflake consulting team to plan the migration of fraud and risk models into Snowpark and rewriting of the scoring code to run in Python inside Snowflake. After implementation, the client was able to retire the exact jobs and removed the copies of customer transaction data from the external scoring environment.

Core Features

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Streaming ingestion on the card and payment transaction feeds, running on dedicated compute.

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Incremental dbt transformations, with tests on every model and changes tracked in version control.

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Regulatory reporting models that map each filed figure to the transaction records behind it.

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Snowpark scoring for the fraud and risk models, running against the tables in Snowflake.

No. of Resource

07

No. of Resource

Time Frame

July 2025 - April 2026

Time Frame

Project Snapshot

HighView Bank Dashboard

Outcomes

Transaction data reaches the fraud and risk teams within seconds of posting, down from 24 hours.

ETL running costs reduced 75% after moving from full nightly reloads to incremental transformations in dbt.

Regulatory figures traced to source transactions from the reporting models, replacing manual evidence gathering.

Fraud model scoring runs 60% faster after removing the extract step.

Customer transaction data no longer copied to an external environment for scoring.

SQL Server warehouse retired with no interruption to reporting.

Technical Stack

Legacy Data Warehouse SQL Server
Cloud data platform Snowflake
Data ingestion Fivetran
Data transformation dbt
Data validation dbt tests
In-platform processing Snowpark
AI and ML functions Snowflake Cortex
Orchestration dbt Cloud
BI and reporting Power BI
Scripting and automation Python
Security and governance Snowflake RBACdynamic data masking
Version control and CI/CD GitGitHub Actions

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