Snowflake Data Engineering

Your data is ready. Your business isn’t using it.

Organizations are drowning in disconnected, unusable data. Meanwhile, AI has shifted from a boardroom discussion to a core business expectation. Because every AI initiative relies on solid data infrastructure, the truth is simple: if your data isn't ready, neither is your AI.

We build Snowflake data environments that your teams can trust, act on, and own — and that are ready for what comes next.

Book a Snowflake Discovery Call Explore Snowflake Use Cases
300+data projects globally 6Snowflake service areas 2production use cases
300+ projects delivered globally
ERPs
SFTPs
APIs
Snowflake
Analytics
Search
AI/ML
Client-owned after go-live
What We Hear

The pattern looks different in every organisation. The problem underneath is the same.

Data teams get pulled into firefighting. Reports that should take minutes take days. Different teams run the same analysis and arrive at different numbers. A new data source gets added and three months later nobody can explain why it breaks the pipeline every Tuesday.

“We have the data. Getting a clean answer out of it takes three days and two people.”

Operations & Finance Leaders

“Our data team spends most of its time keeping pipelines alive instead of building anything new.”

CTOs & Engineering Leads

“We keep getting asked about AI. We know our data foundation is not ready for it yet.”

Data & Technology Leaders
The Operational Reality: The root cause is almost always the same: the data infrastructure was built incrementally, under pressure, by whoever was available at the time. It works until it does not. And by the time it does not, unpicking it is harder than the original problem.
Why Snowflake With Artha

Snowflake is the platform. The operating model still needs design.

We are Artha Solutions — a data engineering and consulting firm. We have delivered over 300 projects globally across data integration, cloud migrations, governance frameworks, and modern data platform builds. Full stack: Snowflake, Qlik, Talend, dbt, Azure, and AWS.

Full-stack expertise across Snowflake, Talend, Qlik, dbt, Azure, and AWS.
Governed, tested, and structured environments designed so client teams can own them after go-live.
AI readiness prepared at the data foundation level, not bolted on as a post-delivery afterthought.
Platform Architecture

The Platform We Design Around Snowflake

Four layers. Each with a single, clear purpose — and specific decisions made at every one.

1

Source Systems

ERPs, SFTPs, APIs, flat files, cloud apps, databases

A typical organisation runs 6–12 source systems, each with its own schema and update frequency. We map every source before writing a line of pipeline code. That upfront work prevents the pipeline rewrite 18 months later.

2

Ingestion Layer

Snowpipe, ADF, Talend, Qlik Replicate, COPY INTO

We choose the ingestion pattern based on the problem. Snowpipe for continuous file-based ingestion. Qlik Replicate for CDC from SQL Server and NetSuite. Talend for complex multi-system orchestration. Azure Data Factory for Azure-hosted pipeline scheduling.

3

Snowflake Platform

RAW to Staging to Mart layers, governance, RBAC

Clear layer separation — RAW zones preserve source data exactly as received, transformation layers run tested business logic, and mart layers serve specific downstream consumers. Role hierarchies, warehouse sizing, and cost controls are configured from day one.

4

Serving & Downstream

BI tools, APIs, Elasticsearch, operational systems

Built backwards from the consumer. Analytics teams need clean documented models. Product APIs need fast structured outputs. Elasticsearch needs data shaped for search retrieval. We build for the actual consumer — which is why the data gets used, not just made available.

Architecture Blueprint
Designed backwards from use

Our framework tracks pipeline mapping directly from ingestion to downstream consumption layers.

Source Systems Mapped First Schema tracking, volume metrics, operational owners
Mapped
Ingestion Patterns Selected Continuous Snowpipe loading, CDC log replication, batches
Ingested
Snowflake Platform Separated RAW landing, tested transformations, mart consolidation
Structured
Serving Layer Optimized Elasticsearch retrieval databases, BI interfaces, AI/ML models
Ready
Who We Work With

Organisations that have outgrown how their data was originally set up.

They have invested in the right systems. The data exists. But getting a reliable answer out of it takes longer than it should — and the team that should be building is mostly maintaining.

01

Different teams, different tools, different numbers from the same data.

02

Reporting cycles take longer than the decisions they support.

03

Data teams are smart but underwater — maintaining instead of building.

04

New systems are added without a plan for how they connect.

05

Data environments work until someone asks a question they were not designed for.

06

AI initiatives stall because the data underneath is not ready.

Industries Served & Relevance

Digital Health & Pharma

Unify pricing, claims, formulary, clinical, and operational feeds into governed Snowflake layers.

Operations & Manufacturing

Connect ERP, quality, supply chain, and production data for trusted operational decisions.

Financial Services

Modernize regulated data environments with controls, lineage, and reliable reporting layers.

Retail & Commerce

Bring customer, inventory, pricing, and channel data together for faster analytics and automation.

Enterprise SaaS

Build product analytics, customer health, billing, support, and AI-ready data foundations.

Life Sciences

Support enterprise integration, governed analytics, and compliant operational data movement.

Six Snowflake Services

Six Things We Do Well. That Is Intentional.

Artha focuses on a defined set of Snowflake services — done properly, not spread thin.

Snowflake Environment Setup

Warehouse architecture, role-based access, schema design, and cost controls — configured for production from day one, not retrofitted after problems appear.

Data Pipeline Design & Build

Connecting your source systems to Snowflake using the right ingestion pattern — batch, CDC, or continuous — for your specific data volume and latency needs.

Transformation Layer & Data Testing

Clean separation between raw, staged, and serving layers with built-in tests that catch broken logic before it reaches your teams. We use dbt where version control and team ownership matter, and Snowflake-native tools where they are the simpler, right choice.

Legacy Migration to Snowflake

Phased migrations from SQL Server, Oracle, or on-premise warehouses. Reporting stays live throughout. No hard cutover, no surprise downtime — validated at every step.

Enterprise System Integration

Bidirectional integrations between Snowflake and ERP, HR, finance, and operational systems — built to run reliably every day, owned by your team after go-live.

Data Governance & Quality

Role hierarchies, access controls, data quality rules, and documentation built into the platform — so every team queries data they can trust, and audits do not become emergencies.

Production Use Cases

Snowflake Use Cases Built for Real Business Operations

These are not pilot architectures. They represent practical Snowflake environments designed around operational reality: messy sources, different update frequencies, downstream consumers, and business-critical trust.

Digital Health · Data Platform

Governed Drug Pricing Platform

Artha unified seven external partner feeds into a governed Snowflake + dbt platform that keeps patient drug pricing current, resolves incompatible source structures, and serves Elasticsearch for fast patient search retrieval.

7partner feeds unified
100K+records processed daily
5dbt transformation layers
Dailyrefreshed pricing pipeline

Tech Stack

Snowflake dbt Azure Data Factory Azure Blob Elasticsearch Files.com
View architecture and operational details
Business risk: If the data is not accurate and current, patients may overpay or walk away without medication.
Problems Solved
Current pricing: Seven partner feeds arrived on different schedules and with different definitions.
Common structure: Pricing, pharmacy, claims, and formulary feeds needed one trusted data layer.
Engineering focus: The team needed fewer manual interventions when partner feeds changed.

Solution Built

Artha built RAW, Staging, Mart Individual, Mart Consolidation, and Mart Final layers in Snowflake with dbt. The consolidation layer resolves incompatible schemas, and Mart Final shapes the output for Elasticsearch rather than only analytical queries.

Integration Flow
7 SFTP Feeds
Azure Blob
ADF
Snowflake + dbt
Elasticsearch
Pharmaceutical · Enterprise Integration

Enterprise Employee Integration Hub

Artha replaced an outgrown Informatica setup with Talend and Snowflake as the central employee data hub, keeping NetSuite, ADP, Greenhouse, Achievers, Vault, and related systems aligned from the same record.

30+integration jobs rebuilt
Day 1new-hire system readiness
1source of truth in Snowflake
LiveNetSuite reporting feed

Tech Stack

Snowflake Talend NetSuite AWS S3 ADP Greenhouse Sage Achievers Vault
View architecture and operational details
Business risk: For a company supporting clinical and regulatory functions, employee data accuracy is a business requirement.
Problems Solved
Platform fit: Informatica licensing cost was disproportionate to what the business needed.
Single record: New-hire data had to reach NetSuite, Greenhouse, Achievers, Vault, and ADP on day one.
Operational visibility: NetSuite reporting needed a real-time analytics feed instead of manually pulled data.

Solution Built

Artha redesigned the integration architecture around Snowflake as the central operational hub. Talend handles the rebuilt flows, NetSuite writes its generated ID back into Snowflake, and a real-time Talend job replicates NetSuite data back for live reporting.

Integration Flow
HRIS / Sage
AWS S3
Snowflake Hub
NetSuite
Downstream Systems
Why Artha

Why Artha for Snowflake Data Engineering

300+ data projects delivered

We have seen most failure patterns before they appear.

Full-stack platform depth

Snowflake, Talend, Qlik, dbt, Azure, and AWS expertise means we recommend what fits, not what we sell.

Live production use cases

Both featured use cases are live in production — not pilots and not proofs of concept.

Builds your team can own

We build what your team can own and modify after we leave — with no ongoing dependency on us.

Honest platform guidance

We will tell you honestly if Snowflake is not the right answer for your situation.

Post-delivery accountability

We are still reachable when something changes six months after go-live.

FAQ & Search Intent

Frequently Asked Questions

AI Summary

Artha helps organisations design, migrate, govern, and operate Snowflake data environments across ingestion, transformation, quality, governance, and downstream consumption. The service is suited for organisations that need reliable analytics, enterprise integration, and AI-ready data foundations.

When reporting takes too long, pipelines break often, legacy warehouses limit scale, or AI and analytics programs are blocked by inconsistent data foundations.

Artha maps each source system, update frequency, volume, latency need, and operational owner before selecting batch, CDC, Snowpipe, COPY INTO, Talend, Qlik Replicate, or Azure Data Factory patterns.

Yes. Artha uses phased migrations that keep reporting live, validate each step, and avoid hard cutovers wherever possible.

Yes, when version control, testing, lineage, and team ownership matter. Artha also uses Snowflake-native tools when they are simpler for the workload.

Artha builds role hierarchies, RBAC, documentation, quality checks, environment separation, and tested transformation logic into the Snowflake platform.

Yes, if the ingestion, transformation, governance, testing, and serving layers are designed for trusted downstream use rather than only ad hoc reporting.

Artha documents the environment, transfers ownership, supports the client team, and remains reachable when sources, requirements, or downstream consumers change after go-live.

Start Your Journey

Not sure where to start?

We offer a free 30-minute discovery call — no pitch, no slide deck. A straight conversation about your data environment, what is slowing you down, and whether Snowflake solves it. If it does not, we will tell you that too.

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