Trusted by industry leaders for data platform modernization
Leverage Snowflake with an experienced and recognized partner
Snowflake is now at the center of modern data strategies, but many enterprises still struggle with siloed systems, high infrastructure costs, and the pressure to prepare for AI-driven initiatives. As a Snowflake partner, N-iX brings proven expertise in designing, migrating, and optimizing Snowflake environments at scale. With over 200 data engineers, we help enterprises modernize their platforms, establish governance frameworks, and prepare high-quality, secure datasets for analytics and AI adoption.
Our Snowflake consulting services span the full lifecycle: strategy and roadmapping, architecture and engineering, migration, security and compliance, performance optimization, and AI/ML enablement. Our Snowflake solutions become a governed, AI-ready foundation for advanced analytics and long-term innovation in your business.
Our Snowflake partner recognitions
Snowflake Select Partner
N-iX has been recognized as a Snowflake Select Partner based on its proven expertise in delivering enterprise-grade Snowflake implementations and modern data platform modernization solutions.
Snowflake AI Data Cloud Services Select Partner
This recognition represents us as a trusted Snowflake consulting partner for enabling AI-driven analytics and advanced data use cases.
Key business goals we help you achieve with Snowflake
Establish a unified and governed data foundation
Enterprises struggle with fragmented data estates where business-critical information is trapped across departments and systems. As a Snowflake Select Partner, we consolidate structured and semi-structured data into a governed environment.
Achieve scalable performance for modern workloads
Legacy warehouses often fail under the pressure of growing data volumes, complex analytics, and concurrent queries from multiple teams. With Snowflake, we design scalable architectures where compute and storage expand independently for high-performance analytics.
Improve cost efficiency and financial predictability
Uncontrolled data growth and inefficient architectures can inflate infrastructure costs and erode ROI. Being one of Snowflake implementation partners, we apply workload governance and consumption-based models that tie costs directly to usage.
Prepare data for advanced analytics and AI adoption
Many enterprises recognize AI’s potential but lack the foundational data environment to adopt it effectively. As a Snowflake consulting company, we establish curated, governed data environments that support predictive analytics, Machine Learning, and Snowflake Cortex AI capabilities.
Our clients’ success stories with Snowflake solutions Case studies
Our Snowflake consulting services We are experts in
Snowflake strategy consulting
Any successful Snowflake program starts with clarity of purpose. We align stakeholders on priorities, quantify ROI, and define the technical and governance guardrails that keep the program predictable and auditable.
- Cloud readiness and data-maturity assessment
- Executive workshops to identify high-value use cases and KPIs
- Adoption roadmap: phases, milestones, budget, and value realization plan
- Target operating model for a data platform
- Architecture and health assessment of current solutions
- Risk register and compliance scope
- Business case and TCO model with cost/benefit scenarios
- Change-management and enablement plan for business and IT stakeholders
Snowflake data architecture and design
As a Snowflake consulting company, we design modern, cloud-native architectures that balance elasticity, performance, and compliance. Our work covers warehouse and schema design, multi-cloud or hybrid deployment strategies, disaster recovery, and governance models.
- Account, environment, and landing-zone design
- Naming standards, database/schema conventions, object lifecycle policies
- Network architecture
- Identity and access
- Data classification and policy design
- Resilience: time travel settings, failover/failback, cross-region replication
- Infrastructure as Code and CI/CD for repeatable releases
- Reference architectures for BI, streaming, and ML on Snowflake
Snowflake data engineering and modernization
We modernize legacy pipelines, build high-quality ELT processes, and enable real-time ingestion to make data consistently available for analytics. The outcome is a modernized data foundation supporting advanced use cases without bottlenecks.
- Ingestion patterns: batch, CDC/streaming
- ELT modeling with testing and documentation standards
- Data contracts and SLAs; schema evolution and versioning strategy
- Data quality and reconciliation: row counts, checksums, anomaly rules
- Orchestration and operational runbooks
- Metadata, lineage, and catalog integration for traceability
- Domain-oriented data products; mesh-friendly patterns where needed
Snowflake migration and implementation
We manage the entire process: from schema and SQL conversion to workload redesign and reconciliation. Within Snowflake consulting and implementation services, our experts design and execute secure, low-risk, and fully validated migrations.
- Source assessment and scoping
- Automated and manual SQL/procedure conversion with gap remediation
- Warehouse/workload redesign to exploit micro-partitioning and services
- Dual-run and cutover planning with defined rollback criteria
- Full reconciliation at scale
- BI/semantic layer alignment
- Performance baselining before and after cutover
- Program governance: status dashboards, decision logs, audit artifacts
Snowflake governance and security
We configure Snowflake with enterprise-grade governance frameworks, including encryption, role-based access controls, data masking, and audit logging. Our Snowflake consulting experts ensure your organization maintains control and auditability.
- Role hierarchy and access patterns for users, services, and partners
- Data masking, tokenization, and privacy-by-design policies
- Activity monitoring, audit logging, and alerting for anomalous behavior
- Compliance controls mapped to GDPR, SOC, PCI DSS, and internal policies
- Secure data sharing, clean rooms, and collaboration controls
- Key management and encryption posture review
- Governance playbooks, approval workflows, and periodic access recertification
Snowflake support and performance optimization
We provide proactive support to keep your environment efficient, secure, and cost-effective. Providing best Snowflake consulting services, we deliver ongoing support to keep your Snowflake environment efficient and predictable. With continuous optimization, your Snowflake platform adapts to evolving business needs and maintains transparency and ROI.
- Query profiling and tuning; warehouse right-sizing and concurrency settings
- Clustering strategy, search optimization, materialized/secure views
- Workload isolation, resource monitors, and consumption guardrails
- Cost dashboards, anomaly detection, and optimization recommendations
- SRE-style observability: health checks, SLIs/SLOs, incident runbooks
- Release management and environment hygiene
- Flexible support models: advisory, enhancement backlog, or managed service
Analytics & AI/ML and data science enablement
Snowflake can be a foundation for enterprise AI. We prepare data environments that enable predictive analytics, Machine Learning, and advanced decision-making. Our services include:
- BI integration and semantic layers
- Curated, governed data marts and feature stores for analytics/ML
- Snowpark development for Python/SQL UDFs; model training/inference patterns
- MLOps handoff: experiment tracking, versioning, deployment approaches
- Real-time and near-real-time analytics where the business case warrants it
- Enablement for Snowflake Cortex under governance
- Adoption playbooks and role-based training for analysts, engineers, and owners
How we guide your Snowflake transformation
We see Snowflake adoption as a transformation program. To keep it structured, low-risk, and measurable, we guide clients through five connected stages.
Align the mission and guardrails
We define what business outcomes Snowflake must deliver, how success will be measured, and what compliance or risk boundaries must be respected.
- Outcomes and KPIs
- Data residency and regulatory scope
- Risk register and success criteria
- Phased roadmap with clear milestones
Architect
Once the goals are clear, the next priority is designing an environment that can scale reliably and pass audits. We establish the architecture, security model, and resilience measures before data moves.
- Org/account layout, environments, landing zones
- RBAC/ABAC, SSO/SCIM, network policies/PrivateLink
- Data classification, masking, row/column policies
- Backup, time travel, and failover strategy
- IaC and CI/CD for reproducibility
Migrate and reconcile
With a stable foundation in place, migration begins. Our approach prioritizes predictability: moving critical datasets first, validating results against the source, and keeping a rollback path open until confidence is established.
- Schema/SQL/proc conversion
- ETL to ELT redesign, CDC, and streaming where relevant
- Dual-run periods and cutover windows
- Reconciliation at scale
- Documented audit trail of migration decisions
Integrate, operationalize, and make it observable
Once data is in Snowflake, we connect it to the broader ecosystem, such as BI tools, pipelines, and orchestration platforms. At this stage, governance, observability, and cost control are implemented to ensure the platform runs smoothly under enterprise SLAs.
- Ingestion patterns and data contracts
- BI integration
- Orchestration, scheduling, and runbooks
- Cost governance with resource monitors and sizing
- Observability: lineage, quality SLAs, usage dashboards
Optimize and extend into AI
After stabilization, the focus shifts to innovation. We fine-tune performance, introduce cost-efficiency measures, and prepare data pipelines for advanced analytics and AI.
- Query profiling, partitioning, and clustering strategy
- Zero-copy cloning for dev/test
- Data sharing, clean rooms, Marketplace adoption
- Feature pipelines and ML-ready datasets
- Role-based training and enablement programs
Why choose N-iX as your Data platform modernization partner?
data engineers specializing in Snowflake, cloud, and analytics
certified Snowflake consultants with hands-on migration and optimization expertise
delivered enterprise data projects across industries
tech professionals supporting large-scale transformations
years of experience delivering data-driven solutions for global enterprises
validated by Select Services and AI Data Cloud Services Select partner status.
Rostyslav Fedynyshyn
Rostyslav Fedynyshyn
FAQ
Working with a Snowflake consulting company ensures faster time-to-value, predictable migration, and enterprise-grade governance. A consulting partner brings proven methodologies, deep technical knowledge, and experience from similar projects. This results in a secure, scalable data platform that supports advanced analytics, improves decision-making, and reduces operational costs.
No. While Snowflake consulting involves technical implementation, it is designed for business decision-makers as much as for IT teams. Executives, department heads, and solution architects rely on consulting to ensure Snowflake aligns with strategic priorities, whether enabling faster reporting, improving compliance, or supporting advanced analytics.
Yes. Snowflake integrates with leading BI platforms like Tableau, Power BI, and Looker, as well as ETL/ELT and data pipeline tools. It also supports APIs and connectors for custom integrations. A consulting partner ensures these integrations are set up correctly, so existing analytics workflows continue without disruption.
After implementation, Snowflake consulting partners typically provide ongoing support, monitoring, and cost optimization. They also deliver training for business and technical users to ensure adoption across the organization. This stage may include documentation, workshops, and governance best practices, so teams can fully leverage Snowflake for analytics and decision-making.
Snowflake creates a unified, governed data foundation for AI initiatives. By consolidating structured and semi-structured data into a single platform, it provides clean, well-managed datasets that can be used by AI and Machine Learning models. With recent capabilities such as Cortex AI and AISQL, we query data more intuitively, simplify pipelines, and accelerate AI-driven projects.
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