Data engineering services scoring framework
Our scoring framework is focused on execution strength (40% - Do they deliver real outcomes?), trust and reputation (30% - What do clients and the market say?), and technical and compliance capability (30% - Are they secure, scalable, and use modern tech stack?). The framework is designed to reward data engineering companies that actually deliver outcomes.
While tech stack compatibility and certifications matter, many vendors can claim them. What separates the best from the rest is their ability to deliver consistently under pressure, especially across large data programs with scope creep, compliance risks, or distributed teams.
That’s why Delivery Excellence and Portfolio Quality make up 40% of the total score. These reflect not just what companies say they can do, but what they’ve proven through public client stories and verified project outcomes.
Best data engineering companies compared
Top data engineering companies - details
STX Next
STX Next is a Europe-based software engineering partner with a deep specialization in data engineering, proven through a portfolio of 200+ data projects across industries including fintech, manufacturing, mobility, healthcare, and logistics. The company helps clients build secure, scalable, and maintainable data platforms tailored to business outcomes.
More about that: STX Next data engineering services.
Why choose them
They combine a vast delivery track record, deep familiarity with modern data stacks, and a strong ability to match architectures to specific industry and business needs. As a certified partner of AWS and Snowflake, and Dekra ISO-certified, STX Next delivers production-ready data solutions with baked-in compliance and reliability.
Best for:
Companies needing a battle-tested partner to build or scale data platforms - be it ingestion pipelines, cloud DWHs, or real-time systems - with industry-fit architectures and full governance oversight.
Key strengths & specializations:
- End-to-end data platform delivery (architecture, implementation, handover)
- Stream/batch data pipelines
- Data warehousing and data lake use builds
- ML enablement pipelines
- Real-time monitoring systems and observability layers
- Custom integrations across cloud, SaaS, and internal sources
Notable projects:
- Predictive maintenance pipelines for global industrial clients
- Manufacturing telemetry platform processing millions of records per day
- Multi-layered data platform for a logistics client, combining batch/stream pipelines, alerting, and BI layer
- Financial data integration and analytics buildouts in fintech
More about that: STX Next case studies.
Key clients:
- Mastercard
- Canon
- Decathlon
- Linde
Reviews & execution excellence:
STX Next scores high on Clutch (4.7/5, 98+ reviews) with repeated praise for on-time delivery, client-centric PM, and high engineering ownership. Their teams are known for clear scope control and reliable roadmap tracking.
Culture:
STX Next fosters long-term partnerships and prioritizes tight collaboration between data engineers, architects, product managers, and client-side product/data leads. The company operates with an agile, well-structured delivery model and maintains strong internal retention and leadership stability.
Top qualities:
- Proven data engineering delivery across 200+ projects
- Platform and partner certified (AWS, Snowflake)
- ISO-compliant practices (via Dekra)
- Delivery governance that ensures scope, timeline, and cost discipline
Industry expertise:
Fintech, logistics, manufacturing, healthcare, industrial IoT, mobility, insurance.
Locations:
Headquartered in Poznań, Poland, with client-facing offices in Houston, London, Eschborn, and a delivery center in Merida, Mexico.
Core tech stack:
AWS, Snowflake, dbt, Airflow, Kafka, Spark, Redshift, PostgreSQL, Python, TypeScript, Docker, Terraform.
Compliance expertise:
Certified by Dekra for ISO 27001/9001; data security and risk awareness are embedded in delivery processes - especially for industries handling sensitive or regulated data.
Delivery models:
End-to-end platform builds, long-running dedicated squads, and product-data team augmentation. All backed by strong internal PMO, QA, and DevOps disciplines.
DataArt
A global software engineering firm with a strong reputation for enterprise-scale data platform modernization and reliable, secure delivery.
Why choose them
Clutch 4.9/5 (26 reviews), strong regulated-industry track record, and deep governance delivery.
Best for:
Complex, regulated environments (e.g., fintech, healthcare) requiring secure, Snowflake/Databricks-powered modernization.
Key strengths & specializations
Cloud platforms and analytics modernization.
Notable projects
- Rappi search re-architecture and loyalty platform modernization
- Royalty processing pipelines in enterprise settings
Reviews & execution excellence
High Clutch rating, with frequent praise for “high-quality deliverables” and proactive engagement.
Culture
People-first, recognized in Newsweek’s “Most Loved Workplaces” and low client-side governance effort.
Top qualities
Governance, security depth, modernization capabilities.
Industry expertise
Retail, fintech, logistics, media.
Locations
US, UK, Germany, UAE, global delivery network.
Core tech stack
Snowflake, Databricks, AWS/Azure/GCP, pipelines (Kafka, dbt, Airflow), Python/SQL.
Compliance expertise
ISO 27001, PCI DSS, GDPR, HIPAA.
Delivery models
Full delivery plus blended staff for long engagements.
ELEKS
Established engineering consultancy (since 1991) offering robust data and analytics platforms with enterprise governance and disciplined execution.
Why choose them
Clutch 4.8/5 (31 reviews), recent ISO 27001/SOC 2 audit, and clear logistics/retail analytics delivery track record.
Best for
Large-scale digital logistics transformations and enterprise-grade BI modernization.
Key strengths & specializations
End-to-end platform builds, logistics analytics, data science integrations.
Notable projects
- Operational logistics platform for Aramex
- Demand forecasting modernization for the car rental business.
Reviews & execution excellence
Clients praise timeliness and structured program management in reviews.
Culture
Quality-conscious with team stability and strong process orientation.
Top qualities
Reliability, deep analytics, agile delivery.
Industry expertise
Logistics, public sector, fintech, insurance.
Locations
EU + UK + North America presence.
Core tech stack
Cloud platforms, BI/analytics services (Azure, AWS, GCP).
Compliance expertise
ISO 27001, enterprise governance.
Delivery models
Turnkey builds and extended teams.
Simform
Cloud-first data engineering partner with a high volume of positive client reviews and strong delivery momentum across analytics and modernization.
Why choose them
Clutch 4.8/5 (~70+ reviews); known for speed, communication, and delivery discipline.
Best for
Scalable cloud-native data platforms (e.g., Snowflake, BigQuery, Redshift) with responsive U.S. support.
Key strengths & specializations
Data warehousing, streaming analytics, data modernization.
Notable projects
- Varied cloud platform builds and analytics implementations.
- Creating a unified behavioral data across eCommerce and events to enable personalized campaigns.
- Modernizing the CDXP™ platform with unified data infrastructure, scalable processing, and marketing automation.
- Modernizing the fulfillment lifecycle (manufacturing).
Reviews & execution excellence
Clients often cite excellent communication and on-budget delivery.
Culture
Client-first ethos with responsive cross-time-zone support.
Top qualities
Agile, pragmatic delivery, modern ecosystem.
Industry expertise
Retail, media, BFSI (case examples).
Locations
US headquartered with global delivery.
Core tech stack
Snowflake, BigQuery, Redshift, Kafka, Airflow, Spark.
Compliance expertise
AWS Partner
Delivery models
Project-bind and dedicated squads.
Intellias
A 3,000+ strong data engineering partner, Intellias delivers large-scale data platforms with strong Telco and Automotive vertical experience and program governance.
Why choose them
Strong engineering maturity, recognized in Zinnov Zones, and repeatable delivery processes in complex environments.
Best for
Automotive, telecom, and mobility companies requiring HD mapping pipelines or real-time platforms.
Key strengths & specializations
Data lakes/lakehouses, governance, telemetry, BI.
Notable projects
- HD-maps on cloud for autonomous systems.
- Telecom BI on Azure with robust analytics.
Reviews & execution excellence
Good Clutch presence with enterprise client praise for timely, governed delivery.
Culture
R&D-oriented, stable teams, long-term relationships.
Top qualities
Scale, oversight, domain in mobility/telecom.
Industry expertise
Telecom, mobility, BFSI.
Locations
EU, UK, US, Middle East.
Core tech stack
Azure/Power BI, cloud DWH, streaming pipelines.
Compliance expertise
AWS Partner, Microsoft Solutions Partner
Delivery models
End-to-end with platform squads.
Adastra
Adastra is a global data, analytics, and cloud consultancy with over 20 years of experience implementing production-grade data engineering solutions across industries.
Why choose them
20+ years of experience, proprietary data migration tools, and strong partnerships with Snowflake, Databricks, AWS, GCP, and Microsoft Azure.
Best for
Enterprises in banking, telecom, energy, retail, and public sectors seeking reliable migration of legacy systems to modern data platforms.
Key strengths & specializations
Cloud-native data platforms, predictive analytics pipelines, metadata governance, real-time streaming systems, AI/ML integration.
Notable projects
- Equa Bank’s full infrastructure migration into Raiffeisen Bank.
- Predictive analytics engine that achieved 80% out-of-stock forecast accuracy.
- Databricks-based gas sales model with ~70% uplift in response rates (E.ON).
Reviews & execution excellence
4.9/5, 12 reviews on Clutch
Culture
Global delivery team with proven methodologies and long-term partnerships; recently backed by Carlyle Group to accelerate international growth.
Industry expertise
Banking, insurance, automotive, energy, telecom, healthcare, retail, public sector.
Locations
Global presence with hubs in Toronto, Prague, Austin, and other delivery centers across Europe and North America.
Core tech stack
Snowflake, Databricks, AWS, Azure, GCP, Airflow, Spark, Kafka, custom tools (MetaCroc, Adoki, Adele).
Compliance expertise
AWS partner, Microsoft Solutions partner
Delivery models
End-to-end projects, platform modernization programs, managed data services, and AI readiness consulting.
ScienceSoft
Veteran software and data engineering firm, delivering secure, structured BI and analytics platforms with ISO-class certifications and mature processes.
Why choose them
Clutch 4.8/5 (39 reviews), certified ISO 9001/27001/13485 - all at enterprise-grade delivery.
Best for
Healthcare, manufacturing, and FS projects where compliance and structured delivery are critical.
Key strengths & specializations
Data warehousing, integration, analytics, and cybersecurity.
Notable projects
- Data warehouse for a diagnostic imaging provider.
- Automated analytics platform to serve multiple healthcare providers.
- Data management platform fot a biotech company.
Reviews & execution excellence
Positive feedback on responsiveness and structured project management.
Culture
Process-driven, quality-assured delivery teams.
Top qualities
Certifications, reliability, multi-industry maturity.
Industry expertise
Healthcare, manufacturing.
Locations
USA, Latvia, Poland, UAE, Lithuania.
Core tech stack
Cloud DWH, BI stacks per client case.
Compliance expertise
ISO 9001/27001/13485 alignment.
Delivery models
Project and team augmentation with solid PMO.
SoftServe
Enterprise-scale consultancy with solid Snowflake and multi-cloud data platform delivery, backed by partner credentials and structured governance.
Why choose them
Snowflake Partner, strong enterprise client base, and committed delivery governance.
Best for
Large organizations needing disciplined, scalable data modernization.
Key strengths & specializations
Snowflake migrations, data governance, analytics engineering.
Notable projects
- Takeoff’s DataOps-powered Snowflake migration.
- Multi-cloud modernization with Google Cloud.
Reviews & execution excellence
Glassdoor and Clutch note structured PM and scale delivery capabilities.
Culture
Process-savvy, enterprise-driven teams.
Top qualities
Platform integration, scale, governance.
Industry expertise
Retail, healthcare, manufacturing.
Locations
USA, Poland, Ukraine, Singapore, Bulgaria.
Core tech stack
Snowflake, GCP, AWS, Azure.
Compliance expertise
AWS Partner, Microsoft Solutions Partner
Delivery models
End-to-end and co-delivery with client teams.
Yalantis
Mid-size engineering partner with very visible data engineering services, detailed tech stack, and practical BI and IoT delivery competency.
Why choose them
Great Project Management; well-defined data engineering line.
Best for
Healthcare, fintech, or logistics firms needing clear ETL pipelines and IoT integration.
Key strengths & specializations
Architecture, batch/stream pipelines, DW, BI, IoT platforms.
Notable projects
- Hospital radiology BI solution.
- IoT network management system.
Reviews & execution excellence
Clients point to clarity and documentation in delivery.
Culture
Strong cross-office coordination (Ukraine, Poland, Cyprus).
Top qualities
Tech clarity, documentation, reliable delivery.
Industry expertise
Healthcare, logistics, fintech.
Locations
Ukraine, Poland, Cyprus.
Core tech stack
Kafka, Spark, Hive/HBase, Snowflake/Redshift.
Compliance expertise
ICO Certified, AWS partner
Delivery models
Project builds and long-term teams.
InData Labs
Boutique data/AI-oriented firm focused on big data engineering. They offer mid-market enterprises agile and outcomes-focused delivery.
Why choose them
High Clutch rating (4.9/5, 18 reviews) and AWS-aligned pipeline delivery.
Best for, key strengths
Retail, logistics, healthcare businesses needing agile, cost-effective engineering.
Key strengths & specializations
Big data pipelines, ETL/data architectures, analytics, ML launch-readiness.
Notable projects
- Data lake implementation for efficient Big Data processing in the financial sector.
- Graph database solution for real-time sales and chemical composition analytics at scale (chemical industry).
Reviews & execution excellence
Fast engagement, lean teams, and client satisfaction emphasized.
Culture
Lean, startup-inspired with strong technical chops.
Top qualities
Agility, technical focus, AWS/ML readiness.
Industry expertise
Retail, logistics, healthcare, finance.
Locations
Cyprus, Miami (US), Vilnius.
Core tech stack
Cloud-native (AWS), Spark/streaming, BI/ML tools.
Compliance expertise
Certified AWS Partner
Delivery models
Project delivery and squad support.
Innowise
Full-cycle engineering firm growing its data engineering services. They combine nearshore efficiency with solid delivery governance and multi-industry reach.
Why choose them
70+ Clutch reviews, active community engagement (Big Data meetups in Warsaw), and a team-first delivery ethos.
Best for
Enterprise data platforms with tight budgets and nearshore coordination needs; cloud migration and implementation.
Key strengths & specializations
Azure integration, BI, DataOps, automated pipelines.
Notable projects
- Environmental data automation.
- Medicine data management platform for streamlined healthcare data analysis.
- Data lake development (banking industry).
- Apache data pipeline (automotive, manufacturing).
Reviews & execution excellence
Value-for-cost, responsiveness, and scalable staffing cited.
Culture
Community-oriented and flexible delivery.
Top qualities
Cost-effective delivery, Azure-first stacks, team flexibility.
Industry expertise
Healthcare, manufacturing, finance, logistics.
Locations
Warsaw HQ + 14 global offices.
Core tech stack
Azure Data Services, Python, BI stacks.
Compliance expertise
ISO Certified, AWS Partner, Microsoft Solutions Partner
Delivery models
Project execution and staff extension.
Vention (formerly iTechArt)
A seasoned engineering services partner with strong ability to rapidly spin up dedicated data squads for product-led organizations.
Why choose them
High-volume Clutch reviews (~90+), experience with modern data ecosystems.
Best for
Startups or mid-market firms needing fast, reliable team building for data projects.
Key strengths & specializations
Cloud migration, data integration, agile scrum-based builds.
Notable projects & reviews
- A neutral network for an e-learning platform.
- Comprehensive historical data analysis and a predictive model for risk analysis and revenue-based funding (Braavo Capital).
Reviews & execution excellence
Clients emphasize adaptability and steady delivery.
Culture
Flexible, product-focused teams with strong client alignment.
Industry expertise
Product/software companies across sectors.
Locations
US HQ (NY) with global delivery centers.
Core tech stack
Cloud-native data stacks (per case).
Compliance expertise
AWS Partner, Google Cloud Partner
Delivery models
Team extension and full-stack delivery.
Your vendor evaluation checklist
Before committing to a data engineering partner, use this checklist to pressure-test their ability to deliver outcomes:
1. Proven delivery
- Have they completed at least 20+ data engineering projects (preferably in your industry)?
- Do they publish case studies with concrete outcomes (e.g. reduced processing time, reliable data processing, lower costs, improved data quality)?
- Are they named in client reviews as on-time, in-budget, and proactive?
2. Execution governance
- Do they show strong scope control, project tracking, and risk mitigation processes?
- Do they assign a dedicated delivery lead / PM?
- Can they share examples of working playbooks or runbooks?
3. Compliance & security
- Are they ISO 27001 or SOC 2 certified, or operating under similar frameworks?
- Have they built data solutions in regulated industries (e.g. fintech, healthcare, energy)?
- Can they demonstrate secure data handling of raw data (e.g. PII masking, RBAC, encryption)?
4. Platform fit & tech stack
- Are they certified or listed as partners with Snowflake, Databricks, AWS, GCP, Azure?
- Have they used dbt, Airflow, Kafka, Spark, Great Expectations (or equivalents)?
- Can they build pipelines for both batch and streaming, ensuring scalable data solutions and reliable data processing?
5. DataOps & governance maturity
- Do they provide automated testing, lineage, observability, and documentation by default?
- Have they implemented cost control, alerting, and CI/CD for data workflows?
- Do they support handover with runbooks, dashboards, and training - critical for long-term data governance?
6. Team composition
- Can they offer named senior engineers, architects, or even data scientists?
- Do they provide stable teams with low turnover?
- Do they operate in time zones that match your working hours?
Red flags to watch for
1. Vague or recycled case studies
- Generic phrases like “delivered a data lake” without business impact or timeline
- No mention of actual tools, architecture, or success metrics
2. Delivery issues hidden in reviews
- Clutch reviews that mention “some delays” or “scope confusion”
- Feedback about poor communication, handover gaps, or rework
3. Overreliance on platform badges
- Snowflake/Databricks/AWS partner logos, but no real delivery examples tied to those platforms and no proof of data warehouse implementation or pipeline success.
4. No security/compliance language
- Not mentioning ISO, SOC, PII, or secure architecture anywhere in their materials
- No regulated clients in their portfolio
5. Limited governance practices
- No mention of Airflow, dbt, testing for modern data engineering, or observability
- No plan for quality, lineage, or failure recovery
6. Misfit delivery models
- Only offers staff augmentation when you need end-to-end delivery
- Long ramp-up or handoff process without agility
Make a confident vendor decision that serves the business
As a tech leader, you're looking for a partner who can:
- Deliver outcomes under pressure
- Handle real-world constraints like budget, compliance, and legacy systems
- Align with both your engineering standards and your company’s strategic goals
That’s why this ranking goes beyond platform badges and surface-level claims. We focused on what matters most to you:
- Reliable delivery under scope, budget, and timeline
- Proven ability to build and scale data platforms
- Experience in industries where governance and security aren’t optional
Whether you’re re-architecting a legacy system, launching a real-time data product, or scaling analytics across the org, the shortlist we’ve provided will help you filter out noise and focus on vendors who can execute well, consistently.
Use the evaluation checklist to dig deeper, and watch for the red flags that signal future risk. And if you're still narrowing down your options, remember that the right vendor isn’t just one that speaks your language - it’s one that can speak to your challenges and solve them with precision.
FAQ: Data engineering services
What is data engineering?
Think of data engineering as the foundation of your company’s data strategy. Data engineering is all about designing and maintaining the systems that collect, store, and transform raw data into something useful for data analytics, machine learning, and AI. Without it, your data is just noise sitting in different silos.
Why are data engineering services important?
Data engineering ensures your business can actually use the information it gathers. It makes data clean, accessible, and reliable, which is crucial for decision-making and compliance. Without strong data engineering, teams often struggle with messy, incomplete, or inaccessible data.
How does data engineering support business growth?
When your data is well-managed, it becomes a real business asset. You can spot trends earlier, optimize operations, and uncover new opportunities. In short: better data leads to better decisions, which leads to growth.
How do data pipelines fit into all this?
Data pipelines are the engines that keep information moving. They take raw inputs from multiple sources, clean and structure them, and deliver them to wherever they’re needed: dashboards, analytics platforms, or AI models. If pipelines aren’t working properly, the whole data strategy slows down.
What role does data engineering play in AI projects?
AI doesn’t run on ideas alone - it runs on data. Moving from a proof-of-concept model to a production-ready AI system requires strong data infrastructure. Without reliable, high-quality data pipelines, even the most advanced AI models won’t deliver real business value.
What are the benefits of modern data engineering?
Today’s data engineering practices rely heavily on automation. That means fewer manual tasks, faster data processing, and more consistent quality. For businesses, this translates into real-time insights and the ability to pivot quickly when market conditions change.
Who’s involved in data engineering projects?
It’s rarely a one-person job. Successful projects often require Data Engineers to build pipelines, Data Architects to design the overall structure, and sometimes Data Platform Engineers to manage cloud environments and scalability. Each role plays a part in keeping data flowing smoothly.
How does data engineering consulting add value?
Data engineering consulting goes beyond building pipelines. A consulting partner helps you define the right data solutions for your business, align with compliance and data governance, and embed best practices. This guidance is especially valuable for organizations scaling quickly or operating in regulated industries where data quality is critical.
What role do data warehouses play in modern data strategies?
A data warehouse implementation is often central to enterprise analytics. By consolidating information into a single hub, businesses can run data analytics with greater speed and accuracy. Many top data engineering companies specialize in data warehouse development, ensuring that platforms are scalable, cost-efficient, and built to handle both batch and streaming data.
Why is data engineering expertise crucial for reliable analytics?
Even the most advanced data analytics tools rely on clean, structured inputs. That’s where data engineering expertise comes in. Engineers transform raw data into trusted datasets using modern data engineering practices, frameworks for data governance, and monitoring tools. The result is improved data accessibility and dependable insights for high-stakes decisions.
What kinds of solutions do data engineering companies provide?
The scope is broad, but typically includes:
- Building scalable and reliable pipelines
- Migrating data to the cloud
- Setting up data warehouses and data lakes
- Improving data quality and governance
- Creating the infrastructure for analytics and machine learning
Essentially, they make sure your business can trust and act on its data.
Why is the cloud so important here?
Cloud platforms like AWS, Google Cloud, and Azure have become the go-to environment for data engineering. They offer the scalability to grow as your data grows, the flexibility to experiment with new tools, and the affordability of pay-as-you-go infrastructure.
Why work with a specialized data engineering company?
Because data at scale is messy. Specialized data engineering companies bring the technical expertise and proven best practices to turn that mess into order. They’ve seen the common pitfalls and know how to design systems that can handle real-world complexity.
How do I choose the right data engineering partner?
Look beyond buzzwords. The right partner should be able to show you real experience handling multi-source data at scale, expertise with modern cloud platforms, and a track record of building pipelines that don’t just work on day one - but scale with your business over time.
