Fortress Payment Automation — Snowflake to Salesforce via AWS
How Iqra Technology replaced a manual daily payment reconciliation process — Snowflake logins, CSV exports, VBA macro cleanup, and manual uploads — with a fully automated, scheduled Snowflake → AWS → Salesforce data pipeline.
The client is a U.S.-based financial services organization specializing in customer retention, loyalty programs, and revenue optimization solutions. They help businesses strengthen customer relationships, improve customer engagement, and drive sustainable business growth through tailored financial and operational strategies.
The client's payment data lived in Snowflake, but getting it into Salesforce required a staff member to manually log in every day, export the previous day's payment records, run a VBA macro in Excel to clean and restructure the data, and then manually upload the cleaned file into Salesforce — a four-step manual chain repeated without fail, every single working day.
As transaction volumes grew across their customer retention and loyalty programs, this manual reconciliation process became increasingly unsustainable — consuming significant staff time, introducing human error risk at every handoff, and creating a fragile dependency on a single daily manual routine. The client engaged Iqra Technology to replace the entire chain with a fully automated, scheduled data pipeline.
Industry Details
IndustryFinancial Services
LocationUnited States
Timeline6 Months
TechnologySnowflake + AWS + Salesforce
Company SizeOver 5,000 Employees
Project Overview
Fortress's payment data lived in Snowflake, but getting it into Salesforce required a person to manually log in, export the previous day's records, run a VBA macro in Excel to clean and structure the file, and then upload it into Salesforce by hand — every single day. This case study covers how that entire chain was replaced with an automated, scheduled data pipeline.
Iqra Technology designed and implemented a fully automated pipeline using custom Snowflake scripts, AWS Lambda functions, and a recurring daily scheduler. The solution extracts the previous day's payment data from Snowflake, cleans and structures it in AWS, and loads it directly into Salesforce — with zero manual steps and no daily human intervention required.
Client Requirements
Automate the daily payment reconciliation workflow end-to-end.
Eliminate manual Snowflake logins and CSV export steps.
Replace the VBA macro data-cleaning process with a code-based solution.
Remove the manual Salesforce upload step entirely.
Run the pipeline on a reliable daily schedule without human intervention.
Ensure the pipeline correctly identifies and pulls the previous day's payment dataset.
Build a robust middleware layer to handle data transformation between Snowflake and Salesforce.
Resolve Snowflake access restrictions that blocked automated query execution.
Problem 1Daily payment data wasn't reliably available in Snowflake in a queryable form for the previous day — the pipeline couldn't consistently identify and pull the correct dataset, making automated extraction unreliable.
SolutionImplemented JavaScript-based query logic within the Snowflake script to explicitly identify and target the correct last-day dataset before processing begins — ensuring every scheduled run pulls the right records regardless of timing variations in when data lands in Snowflake.
Problem 2Salesforce cannot receive data directly from Snowflake, and the existing VBA macro data-cleaning logic couldn't run inside AWS — meaning there was no direct path to automate the transformation and load steps without a compatible middleware layer.
SolutionIntroduced AWS as a dedicated middleware layer: Snowflake delivers data to an AWS Lambda function, which applies the same cleaning and structuring logic previously handled by the VBA macro — now implemented in JavaScript. The transformed records are then pushed into Salesforce via the REST API, removing the VBA dependency entirely and making the full chain cloud-native.
Problem 3Snowflake access restrictions blocked several steps that the automation required — the pipeline could not execute end-to-end because the necessary query and export permissions weren't available to the automated account.
SolutionWorked directly with Snowflake support to identify and resolve the access permission gaps blocking the automated execution path. Once the correct roles and permissions were granted to the pipeline service account, the full unattended pipeline ran successfully end-to-end.
Measurable Results
99%
of the Payment Workflow is Now Fully Automated
The daily manual cycle — Snowflake login, CSV export, VBA macro cleanup, and manual Salesforce upload — has been replaced by a scheduled pipeline that runs end-to-end every day without any manual effort. Finance teams start each morning with clean, up-to-date payment data already in Salesforce.
Implementation Highlights
AWS Lambda Function — built to receive incoming payment data from Snowflake, clean and structure it in JavaScript, and push it directly into Salesforce via REST API, replacing the manual upload step entirely.
Custom Snowflake Scripts — created to extract and prepare the previous day's payment data on schedule, with JavaScript query logic to reliably identify the correct last-day dataset.
Recurring Daily Scheduler — a scheduled trigger runs the complete pipeline automatically every day with no manual intervention at any point in the chain.
VBA Macro Replacement — all data cleaning and structuring logic previously run in Excel is now executed as JavaScript code inside AWS Lambda, making the process fully cloud-native.
Snowflake Access Resolution — worked directly with Snowflake support to resolve permission gaps, enabling full unattended execution of the pipeline service account.
End-to-end pipeline: Snowflake → AWS Lambda → Salesforce REST API — fully automated with zero daily manual touchpoints.
What Our Clients Say
Verified reviews from Clutch.co — rated 4.7/5 across 8 client engagements.
★★★★★
"What stood out most about Iqra Technology was their genuine commitment to delivering quality work and their proactive approach throughout the project. It felt like working with a partner rather than just a vendor."
MJ
Mohd Jaukh
HR Leader, ThinkBiz Technology Pvt Ltd
5.0
Clutch
★★★★★
"Working with Iqra Technology was an excellent experience. They understood our requirements clearly and completed the integration smoothly. The end result has streamlined our operations significantly."
AD
Anonymous Director
Financial Services Company, Australia
5.0
Clutch
★★★★★
"Iqra Technology played a key role in enhancing our internal digital workplace. The portal is now easy to navigate, visually clean, and aligned with our business needs."
AE
Anonymous Executive
DataHeights, Canada
5.0
Clutch
★★★★½
"They performed as promised, communicated regularly, and completed the project on time. All requested data was properly and securely migrated."
TE
Todd
Managing Partner, Emanuel Law Group
4.5
Clutch
★★★★½
"They're flexible and professional. If the company has a tight budget, they're the best company to work with. The team provides good value for money."
GI
Anonymous
Group IT Director, Investment Management, Dubai
4.5
Clutch
★★★★★
"The developer integrated seamlessly into our project, delivering high-quality dashboards that improved decision-making for our clients."
Frequently Asked Questions: Salesforce Payment Automation & Data Pipelines
Payment reconciliation in Salesforce is automated by building a scheduled data pipeline that pulls payment records from your source system (such as Snowflake), passes them through a middleware layer for cleaning and transformation, and loads the structured records into Salesforce automatically via the REST API — removing manual exports, spreadsheet processing, and manual uploads entirely.
Salesforce cannot ingest data directly from Snowflake without a middleware layer. The most reliable approach is to use AWS Lambda as an intermediary: Snowflake delivers data to an AWS Lambda function, which cleans and structures it, then pushes the processed records into Salesforce via the REST API. This decoupled architecture makes the pipeline easier to maintain, debug, and extend.
AWS Lambda acts as the serverless middleware layer in a Salesforce data pipeline. It receives data from the source system, applies transformation and cleaning logic in code, and calls the Salesforce REST API to insert or upsert processed records. Lambda is ideal because it runs on a schedule, requires no dedicated server, scales automatically, and is cost-effective for daily batch operations.
VBA macros used for data cleaning can be replaced with JavaScript or Python logic inside an AWS Lambda function. Every transformation the macro performed — deduplication, field mapping, null handling, data type conversion, column renaming — is re-implemented as code in Lambda and runs automatically as part of the scheduled pipeline without any dependency on Excel or a local desktop.
Scheduled pipelines are typically triggered by an AWS EventBridge rule configured with a cron expression for the required daily run time. When the rule fires, it invokes the Lambda function, which executes the full pipeline — querying Snowflake, cleaning the data, and loading into Salesforce — with no manual intervention required.
AWS Lambda integrates natively with CloudWatch Logs and SNS alerting, meaning any pipeline failure — a Snowflake query error, a transformation exception, or a Salesforce API rejection — is logged automatically and can trigger an email notification to the responsible team. Retry logic can be built into the Lambda function for complex error-handling requirements.
Yes. The Snowflake → AWS Lambda → Salesforce architecture is reusable for any data that needs to move between these platforms on a schedule — customer records, product data, financial summaries, or usage metrics. Contact Iqra Technology at iqratechnology.com/contact-us to discuss your requirements.
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