Data Migration Companies
Data migration companies move your data from a legacy system to a modern cloud warehouse, lakehouse, or database - covering discovery, schema and pipeline re-engineering, validation, cutover, and decommissioning. The firms below are rated Expert or Strong in platform-migration capability in our directory. No vendor payments, no paid placement, no ranking for sale - listed alphabetically, pick by fit.
Fortune 500 organizations running multi-cloud transformations across AWS, Azure, and GCP simultaneously, where a single integrator needs to own the full program.
AccentureAimpoint Digital is the right call for data teams that need a partner credentialed at the elite tier across Snowflake, Databricks, and dbt at once — rare coverage that removes the need to split a modern-stack program across two specialist firms, available from $25K.
Aimpoint DigitalCompare firms with proven platform-migration experience - cloud, data warehouse, and database moves - and learn how migration projects are scoped, sequenced, priced, and de-risked before you commit.
According to DataEngineeringCompanies.com's analysis of 75 migration-capable firms in our verified directory.
Big Bang vs Trickle vs Hybrid: Which Migration Approach
The single most consequential decision in a migration is the cutover approach - it sets your downtime, risk, and budget. Most enterprise programs end up hybrid: move low-risk reporting marts big-bang to build operational muscle, then phase the production-critical flows with a parallel run.
| Approach | How it works | Downtime | Risk | Best for |
|---|---|---|---|---|
| Big Bang | Move everything in one defined cutover window | High (planned outage) | High | Smaller estates, tolerant of a weekend outage |
| Trickle (phased) | Migrate in increments; both systems run in parallel | Near zero | Low | 24/7 systems that cannot take an outage |
| Hybrid | Big-bang low-risk workloads, phase the critical ones | Low | Medium | Most enterprise data-platform migrations |
Phase 1 - Discovery & assessment
Inventory every source, ETL job, BI report, and downstream consumer; capture row counts, refresh cadence, and business owner. Score each workload as rehost, refactor, or rebuild. Typical investment: $25,000-$75,000 over 2-4 weeks. Skipping this is why Gartner estimates most migrations overspend their budgets by 20-50%.
Phase 2 - Schema & pipeline re-engineering
A migration is rarely a like-for-like copy. Source schemas are re-modelled for the target platform (for example, Redshift to Snowflake, or Snowflake to Databricks Lakehouse), and ingestion/transformation pipelines are rebuilt in the target's tooling. This is the largest cost line - typically 40-60% of total project spend.
Phase 3 - Parallel run & validation
Run the new platform alongside the old one for at least one full reporting cycle. Reconcile row counts, control totals, and business-critical metrics before anyone trusts the new numbers. This validation period - not the data copy - is what protects against silent data loss and broken downstream reports.
Phase 4 - Cutover & decommission
Switch consumers to the new platform, keep a tested rollback path until sign-off, then decommission the legacy system to stop paying for two stacks. Document architecture and runbooks so the engagement can wind down without a knowledge cliff. Lingering dual-running is a common hidden cost - budget for a hard decommission date.
Data Migration Companies
75 firms · listed A-ZInclusion criteria: every firm below is rated Expert or Strong in platform-migration capability in our directory assessment. This is a capability cut, not a quality ranking - order is alphabetical.
| Company | Rate | Migration | Best For |
|---|---|---|---|
| 779000 employees | $120-200 | Expert | Fortune 500 organizations running multi-cloud transformations across AWS, Azure, and GCP simultaneously, where a single integrator needs to own the full program. |
| 100 employees | $125-200 | Expert | Financial services and enterprise data platform implementations |
| 200 employees | $175-275 | Expert | Aimpoint Digital is the right call for data teams that need a partner credentialed at the elite tier across Snowflake, Databricks, and dbt at once — rare coverage that removes the need to split a modern-stack program across two specialist firms, available from $25K. |
| 100 employees | $150-220 | Strong | Custom connector development and large-scale data replication |
| 200 employees | $75-125 | Strong | Data engineering and analytics; distributed data processing |
| 100 employees | $100-200 | Expert | Mid-market companies needing end-to-end data solutions; data modernization projects |
| 100 employees | $150-250 | Expert | Snowflake and Salesforce integration; AI-native consulting |
| 2500 employees | $50-99 | Strong | Regulated industries; nearshore teams; life sciences and finance |
| 1500+ employees | $250+ | Strong | Private equity firms and portfolio companies requiring due-diligence-grade analytics strategy on Snowflake, where Bain's PE relationships and $400K+ engagement model are already embedded in the deal process. |
| 2500+ employees | $250+ | Strong | Boards and executive teams commissioning a deep-tech or AI venture build through BCG X, where the engagement is strategic investment rather than data engineering delivery. |
| 500 employees | $100-150 | Strong | Microsoft technologies and PowerBI consulting; .NET development |
| 50 employees | $150-250 | Expert | Open-source big data; Elasticsearch and OpenSearch specialists |
| 100 employees | $75-150 | Strong | Asian markets; Microsoft Azure and PowerBI specialists |
| 100 employees | $125-200 | Strong | Bluecloud is the right fit for mid-market companies modernizing to a cloud data stack on Databricks or Snowflake with AWS or Azure — a 100-person size keeps engagement management lean while a $125–200/hr rate reflects genuine modern-stack expertise rather than generalist consulting margins. |
| 70 employees | $160-240 | Strong | Brooklyn Data (now part of Velir) is the right choice for companies building or maturing a dbt-centered modern data stack with Snowflake, Looker, and Fivetran — its 70-person full-stack specialization in that ecosystem delivers tighter engagements than a generalist at $40K+. |
| 300000 employees | $75-150 | Expert | European industrial and engineering-intensive enterprises running Industry 4.0 or R&D data programs where manufacturing-domain depth and on-continent delivery are requirements. |
| 1000 employees | $50-100 | Expert | Microsoft Azure specialists; PowerBI and AI solutions |
| 340000 employees | $75-150 | Expert | Fortune 2000 retailers and consumer-goods companies running GenAI modernization programs that need a large delivery bench and established enterprise relationships. |
| 2500+ employees | $200+ | Strong | Enterprise-scale event streaming and data in motion |
| 100 employees | $150-250 | Expert | Financial services data cloud; Snowflake Premier Partner |
| 500 employees | $50-100 | Strong | Enterprise data modernization; Big Data solutions |
| 80 employees | $125-200 | Strong | Modern data stack implementation and analytics engineering |
| 3000 employees | $50-100 | Strong | Custom software development with data engineering; European nearshore |
| 30 employees | $140-220 | Strong | dbt implementation and analytics engineering workflow optimization |
| 60 employees | $125-200 | Strong | Data governance and managed data services |
| 50 employees | $100-175 | Expert | Datapao is the right choice for European companies running Databricks on Azure or AWS that need MLOps architecture and Spark/Kafka expertise — Databricks Premier Partner status since 2017 and a 50-person focus mean buyers get senior practitioners, not rotated generalists, at $100–175/hr. |
| 50 employees | $100-175 | Strong | AI-driven data engineering and MLOps implementation |
| 50 employees | $100-175 | Expert | Dateonic is the right call for a team building or scaling a Databricks or MLflow-based ML platform on AWS, Azure, or GCP — 50 specialists available from $100–175/hr with a $25K minimum engagement. |
| 400 employees | $200-300 | Strong | dbt Labs is the definitive choice for organizations migrating legacy analytics engineering to dbt, standardizing dbt practices across a data organization, or requiring training directly from the team that built and maintains the tool — at $200–300/hr. |
| 450000 employees | $75-175 | Expert | Regulated-industry enterprises — healthcare systems, banks, insurers — that need C-suite advisory, compliance framing, and Big Four sign-off alongside the technical delivery. |
| 11000 employees | $100-175 | Expert | European enterprises; cloud and cybersecurity specialists |
| 150 employees | $50-99 | Strong | AI and data analytics for global brands; GenAI solutions |
| 100 employees | $75-150 | Expert | End-to-end data engineering; data lakehouse implementations |
| 5000+ employees | $175+ | Expert | Global compliance, audit-ready data platforms, and finance transformation |
| 1000 employees | $200+ | Expert | Modern data ingestion strategy and connector configuration |
| 5000 employees | $100-200 | Strong | Enterprise AI and decision intelligence; Fortune 500 companies |
| 150 employees | $140-220 | Expert | Hakkoda is the right fit for healthcare and financial-services teams building cloud-native data platforms on Snowflake where domain compliance expertise matters as much as engineering — at $140–220/hr with a $50K minimum, the specialization comes without the overhead of a global SI. |
| 200 employees | $150-250 | Expert | Enterprises needing cloud migrations and IoT data solutions |
| 10000+ employees | $50-125 | Expert | Large-scale legacy migrations and managed services outsourcing |
| 500 employees | $125-200 | Strong | Software consultancy with data engineering; Agile delivery |
| 3000 employees | $50-100 | Expert | Product engineering with data modernization; Digital assurance |
| 70 employees | $140-210 | Expert | Infostrux is the right choice for data teams adopting Data Vault 2.0 on Snowflake with dbt — its 70-person pure-play focus means the methodology is the firm's core practice, not an add-on service, available from $40K. |
| 300000 employees | $50-100 | Expert | Global enterprises; offshore development model; large-scale implementations |
| 2500 employees | $50-100 | Strong | Full-cycle software development with data engineering; Eastern Europe |
| 3000 employees | $50-100 | Strong | Automotive, fintech, and large-scale engineering projects |
| 500 employees | $150-275 | Expert | BI and analytics deployments; Tableau and Snowflake specialists |
| 3500 employees | $50-100 | Strong | VC-backed startups and rapidly scaling tech firms |
| 3000 employees | $50-100 | Strong | Mid-market companies; full-cycle software development with data engineering |
| 200 employees | $75-150 | Strong | Intelligent automation and data analytics; Microsoft Azure specialists |
| 4000+ employees | $175+ | Expert | Risk management, regulatory reporting, and finance back-office data |
| 50 employees | $150-225 | Expert | Companies seeking Snowflake-to-Databricks migration; cloud data platform specialists |
| 5000+ employees | $55-130 | Expert | Snowflake migrations for large enterprises |
| 900 employees | $150-250 | Expert | Australia/NZ enterprises; Elite Databricks Partner; regulated industries |
| 2000+ employees | $250+ | Strong | Large-scale digital transformation and strategy-led AI initiatives |
| 4000+ employees | $50-125 | Expert | Banking and capital-markets firms running structured data modernization programs on Snowflake where financial-services domain expertise is a baseline requirement. |
| 2400 employees | $50-100 | Expert | European nearshore development; Fortune 500 clients |
| 5000 employees | $125-200 | Expert | Digital transformation; enterprise data and analytics |
| 500 employees | $150-250 | Expert | phData is the right call for mid-enterprise teams running or planning a Snowflake migration at $100K+ scale — its 500+ completed migrations and Snowflake Elite status translate into lower risk and faster time-to-value than a generalist SI at the same rate band. |
| 100 employees | $50-100 | Strong | Data engineering and analytics for startups and mid-market |
| 100 employees | $125-200 | Expert | Data consultancy and bioinformatics; enterprise data mesh |
| 6000+ employees | $175+ | Expert | Busines-led transformation and finance function modernization |
| 500 employees | $75-150 | Strong | Microsoft Azure specialists; Industrial IoT and smart machines |
| 700 employees | $50-100 | Strong | Healthcare and financial services; compliance-focused data solutions |
| 1000 employees | $50-150 | Strong | Sigmoid is the right call for mid-market companies that need ML engineering and data platform work across Snowflake, Databricks, and the major clouds without paying top-of-market rates — a $50–150/hr range makes serious ML work accessible at a $25K+ entry point. |
| 500 employees | $50-100 | Strong | Simform is the right call for a startup or enterprise that needs a 500-person digital product shop to own both the application layer and its cloud-native data infrastructure — AWS, Azure, GCP, Databricks, and Snowflake — under one engagement starting at $25K. |
| 13000 employees | $150-250 | Expert | Large enterprises running AWS-anchored digital transformation programs — particularly those involving GenAI — where Slalom's AWS GenAI Partner of the Year status and 13,000-person delivery model are differentiating factors. |
| 2100 employees | $125-200 | Expert | Nordic companies; Snowflake Elite Partner; data-driven transformation |
| 500 employees | $75-150 | Expert | European nearshore; fintech, manufacturing, logistics; 200+ data projects; AWS & Snowflake certified |
| 600000 employees | $50-100 | Expert | Multinational enterprises running large-scale, multi-year data platform transformations where offshore delivery economics and a 600,000-person bench matter more than specialist depth. |
| 8000+ employees | $45-120 | Strong | Telecom operators and large manufacturers running multi-year data platform programs where offshore delivery economics and domain-specific process knowledge are primary selection criteria. |
| 10000 employees | $150-250 | Expert | Organizations adopting data mesh as an architectural pattern who need the team that originated and operationalized the approach at enterprise scale. |
| 3000 employees | $100-200 | Strong | Tiger Analytics is the right call for large retailers and CPG companies that need advanced analytics, AI/ML, and GenAI capability at enterprise scale — a 3,000-person bench and GenAI accelerators support programs smaller specialist firms cannot staff, at $100–200/hr. |
| 3000 employees | $100-200 | Expert | Tredence is the right call for retail and CPG enterprises running large-scale analytics or GenAI programs where accelerators that cut migration timelines by 50%+ have a measurable ROI — a 3,000-person bench supports the staffing depth those programs require at $100–200/hr. |
| 200000 employees | $50-100 | Expert | Large-scale global enterprises; offshore delivery model |
| 500 employees | $50-100 | Expert | Agentic AI systems; real-time analytics; platform engineering |
Types of Data Migration
"Data migration" covers four distinct project types, each with different tooling and risk. Match the firm's track record to the move you actually need.
Cloud migration
On-prem or data-center workloads to AWS, Azure, or GCP. See AWS, Azure, and GCP partner guides.
Data-warehouse migration
Legacy warehouse to a modern platform - for example a Snowflake to Databricks migration, or onto Snowflake.
Database migration
Transactional database moves (Oracle, SQL Server, Postgres) with schema conversion and replication-based cutover.
Platform re-architecture
Consolidating tools and re-modelling for a lakehouse, often to unlock AI/ML workloads.
Frequently Asked Questions
What does a data migration company do?
A data migration company plans and executes the move of data from a source system to a target platform - cloud warehouse, lakehouse, or database. The work spans discovery and inventory, schema and pipeline re-engineering, choosing a migration approach (big bang, trickle, or hybrid), parallel-run validation, cutover, and decommissioning the legacy system. The goal is a controlled transition with no data loss and minimal downtime, not just copying tables.
How much does data migration cost?
Based on DataEngineeringCompanies.com's analysis of 75 migration-capable firms, hourly rates range from $45-$250/hr (avg $108/hr). A scoped data-warehouse migration typically runs $75,000-$300,000; a multi-source cloud migration with pipeline re-engineering runs $150,000-$750,000+. Cost is driven by data volume, number of source systems, transformation complexity, and tolerable downtime during cutover.
What is the difference between big bang and trickle migration?
Big bang migrates everything in a single defined window - faster and cheaper, but higher risk and requires downtime. Trickle (phased) migration moves data in increments while both systems run in parallel - lower risk and near-zero downtime, but longer and more expensive. Hybrid moves low-risk reporting workloads big-bang and phases the production-critical flows; most enterprise migrations are hybrid.
How long does a data migration take?
A single data-warehouse migration typically takes 3-6 months; a multi-source enterprise cloud migration with pipeline re-engineering takes 6-18 months. The timeline is set by the number of source systems, downstream dependencies, data-quality remediation, and the parallel-run validation period - not by raw data volume alone.
How do you reduce risk in a data migration?
Inventory every source, pipeline, and downstream consumer before moving anything; profile and remediate data quality at source; run the new platform in parallel for at least one full reporting cycle and reconcile row counts and business totals; sequence low-risk workloads first; and keep a tested rollback path until cutover sign-off. Skipping parallel-run reconciliation is the most common cause of post-migration trust failures.
Deep-Dive Guides
In-depth research articles supporting this hub.
Data Migration Best Practices: A Technical Blueprint for 2026
Explore data migration best practices for a smooth, low-risk transition. Learn planning, testing, and post-migration steps in this practical guide.
Read guideYour Cloud Migration Assessment Checklist: A Practical 10-Point Framework
Discover the cloud migration assessment checklist to plan cost, security, data, and vendor decisions for a successful 2026 migration.
Read guideActionable Playbook for Snowflake to Databricks Migration
Actionable playbook for engineering leaders: Snowflake to Databricks migration. Strategies for cost, execution & AI/ML value.
Read guideA Pragmatic Guide to Cloud Migration Consulting Services for Data Leaders
A practical guide to cloud migration consulting services. Learn to choose partners, manage costs, and execute a successful data platform modernization.
Read guideFind a Data Migration Partner
Use our matching wizard to find firms with proven cloud, warehouse, and database migration experience for your source systems and target platform.
Want the broader picture first? The top data engineering companies in our independent 2026 directory are profiled by rate, capability, and engagement fit.
Compare Migration Firms