Choosing the Right Data Engineering

Modern enterprises are rapidly moving away from legacy ETL pipelines toward ELT-first architectures on Snowflake and Databricks. 
The shift promises scalability, lower costs, and faster analytics—but only if executed correctly. In practice, many modernization programs stall due to poor partner selection, underestimating governance complexity, or misaligning tools with business needs.
Choosing a data engineering consulting partner today is a high-risk, high-impact decision. 
The wrong choice can lead to cost overruns, fragile pipelines, low analytics adoption, and long-term platform debt. 
This article provides a structured framework to evaluate consulting partners for ETL-to-ELT modernization, Snowflake and Databricks migrations, and ongoing optimization—with a clear lens on outcomes, risk, and long-term value.
Perceptive’s POV:
At Perceptive Analytics, we believe successful ELT modernization is not about moving faster—it’s about moving deliberately. The best partners combine deep platform expertise (Snowflake, Databricks, Power BI) with strong governance, realistic timelines, and continuous optimization. Modern data platforms fail not because of tools, but because partners treat migration as a one-time project instead of a living analytics system.

What defines a top data engineering consulting partner today?
Not all data engineering consulting firms are built for modern ELT architectures. The best partners demonstrate repeatable success across platforms, pipelines, and governance models.
Key criteria to evaluate
Proven enterprise modernization track record

Multiple ETL-to-ELT transformations, not first-time experiments
Experience across regulated and high-scale environments

Clear differentiators beyond staffing

Defined methodologies for ELT, not just “resources on demand”
Reusable frameworks, accelerators, or reference architectures

Modern ELT tooling expertise

Deep experience with Snowflake, Databricks, dbt, Fivetran, cloud-native orchestration
Understanding of ELT cost and performance trade-offs

Complex migration capability

Handling schema drift, historical backfills, and parallel run strategies
Proven approach to minimizing downtime and business disruption

Analytics-first mindset

Designs optimized for BI, Power BI, and downstream analytics consumption

Evaluating success rates, timelines and risk for ETL-to-ELT modernization
Modernization projects fail most often due to overpromising timelines and underestimating risk.
Questions to ask potential partners
What is your success rate with ETL-to-ELT modernization?

Look for phased delivery metrics, not just “go-live” claims

What are typical delivery timelines?

ELT foundation: weeks, not months
Full migration: phased over quarters

Snowflake and Databricks migration experience

Number of completed migrations
Scale of data and workload complexity

Risk identification and mitigation

Parallel runs, rollback strategies, blue-green deployments

Change management and adoption risk

How analytics teams are enabled post-migration

Comparing consulting partners for Snowflake, Databricks and Power BI
Most large consultancies and system integrators can “support” Snowflake and Databricks. Fewer specialize deeply enough to optimize performance, cost, and BI adoption.
What to compare across partners
ELT pipeline tooling expertise

Snowflake-native ELT patterns
Databricks lakehouse architectures
dbt and modern transformation workflows

Migration depth

Legacy ETL tools → Snowflake/Databricks
On-prem to cloud data platforms

Snowflake implementation experience (Perceptive Analytics)

Analytics-ready modeling
Cost and performance optimization
Secure multi-team access patterns

Power BI expertise (Perceptive Analytics)

Semantic modeling aligned with Snowflake
Performance tuning for enterprise BI
Governance at scale

Cloud specialization

Clear focus vs “all clouds, all things” approaches

Methodologies and accelerators

Prebuilt templates, QA frameworks, and migration playbooks

Governance, quality and ongoing optimization: how firms really differ
Governance and quality separate successful platforms from expensive failures.
Evaluation criteria
Governance frameworks

Alignment with DAMA-DMBOK principles
Clear ownership models and access controls

Data quality assurance

Automated testing
Data freshness and completeness checks

Industry standards alignment

CI/CD for data pipelines
Observability and lineage

Ongoing optimization model

Cost tuning for Snowflake and Databricks
Performance optimization as usage grows

Adaptability to new technologies

AI, ML, and GenAI readiness

Perceptive POV:
Governance is not a compliance checkbox—it is the foundation for scalable analytics and AI trust.
Cost, pricing models and long-term value
Cost comparisons must go beyond hourly rates.
What to assess
Pricing models

Fixed-scope vs outcome-based vs managed services

Cost efficiency and ROI

Reduced pipeline failures
Faster analytics delivery

Perceptive Analytics value proposition

Predictable delivery
Lower rework through analytics-first design

Market comparison

Large SIs: higher overhead, slower iteration
Specialized firms: focused teams, faster value

Long-term cost implications

Platform sprawl
Ongoing optimization vs stagnation

Case Study
Perceptive Analytics helped a global B2B payments platform with over 1M customers across 100+ countries modernize its data pipelines by integrating CRM data with Snowflake. The client lacked any automated ETL process, leading to inconsistent customer records, delayed updates, and heavy manual effort across teams.

90% reduction in ETL runtime (45 minutes to under 4 minutes)
30% faster CRM data synchronization
Fully automated, reliable data flows across CRM, Snowflake, and BI tools
Improved trust in customer data for operations, reporting, and decision-making
This engagement highlights Perceptive Analytics’ strength in Snowflake-centric ELT modernization, performance optimization, and governance-first data engineering.
How Perceptive Analytics fits among leading data engineering consulting firms
Across success rates, governance rigor, cloud specialization, pricing, and optimization, Perceptive Analytics consistently aligns with enterprises that prioritize analytics outcomes over infrastructure checklists.
Key strengths include:
Deep Snowflake and Power BI expertise
Strong governance and data quality frameworks
Predictable delivery for ELT modernization
Ongoing optimization, not one-off projects
A focused, senior delivery model rather than layered staffing

Perceptive competes effectively with larger firms while offering the agility and specialization many enterprises now require.

1. Decision checklist for shortlisting your data engineering partner Use this checklist when building your shortlist or RFP: Proven ETL-to-ELT modernization success Deep Snowflake and/or Databricks expertise Clear governance and data quality framework Realistic timelines and risk mitigation plans Transparent pricing and ROI model Strong Power BI and analytics alignment Evidence: case studies, certifications, ratings Ongoing optimization and support capability Conclusion Modern data platforms succeed when architecture, governance, and analytics adoption move together. Use the criteria above to narrow your shortlist to partners who can deliver not just migration—but sustained value. When Snowflake, Power BI, governance, and long-term optimization are priorities, Perceptive Analytics is a strong partner to evaluate. At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. As a leading power bi consulting company, we provide trusted services with experienced Microsoft Power BI consultants, turning data into strategic insight. We would love to talk to you. Do reach out to us.