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data engineering services

Data Engineering Consulting Services That Deliver Production Platforms in Months, Not Years

For Data & Technology Leaders tired of choosing between slow internal builds, risky big-bang migrations, and vendors who disappear after go-live. Get a proven path to modern data platforms that your team actually owns.

Get Your Free Data Readiness Assessment
data engineering consulting

Your Data Infrastructure Shouldn't Be Your Competitive Bottleneck

Talent Trapped Firefighting

Your best data engineers spend 60% of their time on integration duct tape Instead of building the AI and analytics capabilities your business needs, they're maintaining legacy pipelines and fighting data quality fires.

Competitive Ground Lost

Every quarter you delay, competitors get 90 days closer to AI-driven decision-making While you're stuck in analysis paralysis, others are already using their data as a competitive weapon. The cost of inaction compounds daily.

Spending More, Delivering Less

Legacy systems + cloud sprawl = rising costs with flat business value Your infrastructure budget keeps growing, but business stakeholders still can't get the insights they need when they need them.

You've already decided you need to modernize. The real question is how—without betting the business on an 18-month death march project that might never deliver.

See how we de-risk data modernization

Data Engineering Consulting Services That De-Risk Your Modernization Journey

We're not here to create dependency. We're here to build your capability, with transparent timelines, clear economics, and architecture you actually own.

Get to Production in Quarters, Not Years, With Architecture That Scales

Speed Without Shortcuts

Get to Production in Quarters, Not Years, With Architecture That Scales

Stop choosing between speed and quality. Our data science engineering services use proven frameworks that accelerate delivery without accumulating technical debt. You get working infrastructure in 90-180 days, not promises in PowerPoint presentations.

  • Defined delivery milestones with timeline commitments
  • Phased implementation approach (crawl-walk-run)
  • Reusable patterns that accelerate subsequent projects
  • Production-ready code from day one, no "we'll fix it later" shortcuts
See our 120-day platform build case study

Risk-Reversed Engagements

Start Small, Prove Value Fast, Scale with Confidence

No more betting the business on unproven vendors or untested approaches. Our data engineering consultants work in phases with clear exit ramps and success metrics at every stage. If we don't deliver, you don't proceed. It's that simple.

  • Assessment-first approach with no-obligation diagnostic
  • Pilot projects with defined success criteria (8-12 weeks)
  • Transparent phase-gate decisions, you control what happens next
  • Open standards architecture (no proprietary lock-in)
  • Zero-downtime migration strategies, your business keeps running
Learn about our phased engagement model
Start Small, Prove Value Fast, Scale with Confidence
Know What You're Investing Before You Commit

Economics That Make Sense

Know What You're Investing Before You Commit

No more scope creep surprises or hidden costs. Our data engineering consulting company provides transparent pricing with detailed ROI models that account for your current team's reality. Compare the true cost of building internally, buying platforms, or partnering with us, then decide.

  • Indicative investment ranges by project type ($150K-$800K+, depending on scope)
  • Total cost of ownership comparison: build vs. buy vs. partner
  • Talent cost arbitrage vs. hiring 5 senior data engineers ($1.5M-$2M annually)
  • Quantified cost of delayed modernization
  • Flexible engagement models (project-based, retainer, hybrid)
Download our free ROI calculator

Compliance-First, Not Compliance-Later

Built-In Governance for GDPR, CCPA, and Industry Regulations

Retrofitting compliance costs 10x more than building it right from the start. Our data engineering services include governance, security, and regulatory frameworks from day one, because your data platform needs to pass audits, not just performance tests.

  • Regulatory framework expertise (Financial Services, Healthcare, Energy)
  • Data lineage and audit trail architecture
  • Multi-geography data residency handling
  • Security-by-design methodology
  • Compliance documentation as a standard deliverable
See our healthcare compliance case study
Built-In Governance for GDPR, CCPA, and Industry Regulations
We Build With Your Team, Not Around Them

Your Team Gets Stronger, Not Sidelined

We Build With Your Team, Not Around Them

The goal isn't to make you dependent on us, it's to make you self-sufficient. Our data engineering consultants embed with your team, transfer knowledge continuously, and deliver comprehensive documentation. When we're done, you own the platform AND the expertise to run it.

  • Embedded team model (not offshore black box)
  • Structured knowledge transfer program throughout engagement
  • Internal capability assessment and upskilling roadmap
  • Complete documentation and runbook delivery
  • Optional ongoing advisory retainer (not forced managed services)
Learn about our knowledge transfer approach

Folio3 Data Engineering Services & Solutions

Your data infrastructure needs aren't generic. Neither are our data engineering solutions. We are data engineering service providers, and we solve specific business problems without delays.

Data Architecture & Strategy

Build once, scale forever. Our architects design cloud-native data platforms that grow with your business, no retrofitting required.

  • Automated ETL that eliminates manual data wrangling
  • Database optimization that cuts query times by 10x
  • Seamless system integration across legacy and modern stacks

Business Impact: Your team accesses insights in minutes, not days.

Data Lake Engineering Services

Stop drowning in unstructured data. We build and implement data lakes that turn chaos into clarity, perfect for product traceability, customer 360 views, and IoT analytics.

  • Scalable storage that handles explosive data growth
  • Fast processing that keeps pace with real-time demands
  • Query-ready architecture from day one

Business Impact: Unlock analytics capabilities your current systems can't support.

Data Pipeline Engineering

Your data flows 24/7. So should your pipelines. We automate ETL/ELT processes using Databricks, Informatica, and best-in-class orchestration tools.

  • Real-time and batch processing that fits your use case
  • Self-healing pipelines with automated error handling
  • Monitoring dashboards that surface issues before they break

Business Impact: Data reliability goes from 85% to 99.9%+.

ETL/ELT & Data Integration Engineering Services

Extract data from anywhere. Transform it for anything. We handle REST APIs, FTP servers, databases, and even legacy systems with proprietary protocols.

  • Batch and real-time stream processing
  • Scalable data pipelines built for volume spikes
  • Integration solutions that don't break when systems change

Business Impact: Your data engineers stop firefighting and start building.

Data Warehouse Modernization

Centralize structured data for lightning-fast querying, reporting, and analytics. Our architects design warehouses that scale without performance degradation.

  • Data modeling and schema design optimized for your queries
  • Metadata management that makes data discoverable
  • DWH migration with zero downtime
  • Analytics and reporting that business users actually use

Business Impact: Reporting cycles shrink from weeks to hours.

Data Analytics Engineering Services

Turn data into decisions. We develop analytical methods, recommendation systems, and forecasting models that guide strategic choices.

  • Pattern analysis that reveals hidden opportunities
  • Data quality evaluation that improves model accuracy
  • Strategic forecasts tied to business KPIs

Business Impact: Executive decisions backed by data, not gut feel.

ML Engineering & Model Operations

Get AI projects from concept to production, globally. We manage the full ML lifecycle with scalable infrastructure that supports model deployment at enterprise scale.

  • End-to-end model management and versioning
  • Automated retraining pipelines
  • Global deployment architecture
  • Monitoring systems that catch model drift early

Business Impact: ML models that deliver ROI, not just science experiments.

DataOps & Governance

Minimize downtime. Maximize data availability. We implement DataOps practices that bring DevOps rigor to your data operations.

  • CI/CD pipelines for data code
  • Automated testing and validation
  • Comprehensive monitoring and alerting
  • Data governance frameworks that pass audits

Business Impact: Your data platform becomes as reliable as your production apps.

Data Fabric Architecture

Create a unified data layer across distributed environments. We build data fabrics that enable self-service access without data movement.

  • Augmented data catalog with intelligent search
  • Knowledge graphs that map data relationships
  • Metadata activation for automated workflows

Business Impact:Data democratization without security compromises.

Data Mesh Implementation

Decentralize data ownership while maintaining governance. We implement data mesh architectures that empower domain experts.

  • Domain-driven data ownership models
  • Federated learning infrastructure
  • Cross-domain data transformation pipelines

Business Impact: Domain teams move faster without central bottlenecks.

Big Data Platform Engineering

Process terabytes or petabytes with ease. Our big data engineering services handle massive scale on cloud-native, cloud-agnostic, or on-premises infrastructure.

  • High-performance data ingestion and storage
  • Distributed processing and cleaning at scale
  • Advanced analytics and visualization
  • Pipeline orchestration that manages complexity

Business Impact: Handle 10x data growth without 10x cost increases.

Why Data Leaders Choose Our Data Engineering Services Over Building In-House or Hiring Traditional Consultants

Internal Build

Timeline: 18-24 months

(if you can hire the team)

Investment: $2M+ in annual salaries alone

Risk Factors

  • Hiring cycle delays (6-12 months per senior hire)
  • Retention challenges in competitive market
  • Knowledge loss when team members leave
  • Learning curve on every architectural decision

Bottom Line:
High cost, high risk, slow time-to-value

Traditional Consulting Firms

Timeline: 12-24 months

(heavy on strategy, light on execution)

Investment: Unclear until you're deep into the engagement

Risk Factors

  • 200-slide strategy decks, limited working code
  • Proprietary frameworks that lock you in
  • Junior resources doing the actual work
  • Knowledge walks out the door when contract ends
  • Scope creep and change order surprises

Bottom Line:
Expensive, dependency-creating, unclear ROI

Our Data Engineering Consulting Company

Timeline: 90-180 days

to production-ready infrastructure

Investment: Transparent pricing ($150K-$800K+ based on scope)

Our Approach

  • Working code in weeks, not strategy documents
  • Open standards, you own everything we build
  • Senior architects hands-on throughout
  • Continuous knowledge transfer to your team
  • Fixed-price pilots with clear success metrics

Bottom Line:
Fast, transparent, capability-building

Talk to Our Specialist About Implementation

We're not here to create dependency. We're here to build your capability. If building internally makes more sense for your situation, we'll tell you that in the assessment phase.

Schedule Your Assessment

Industries We Serve

We don't just understand data engineering, we understand your industry's business and its unique challenges, compliance requirements, and competitive pressures.

Healthcare

HIPAA-compliant data platforms, PHI de-identification pipelines, clinical analytics infrastructure, and patient 360 views

Financial Services & Insurance

Regulatory reporting automation, fraud detection pipelines, risk analytics platforms, and customer 360 data models

Agriculture

IoT sensor data platforms, crop yield prediction models, supply chain traceability systems, and precision agriculture analytics

Media, Entertainment & Gaming

Real-time player analytics, content recommendation engines, streaming telemetry platforms, and audience behavior analysis

Sports

Performance analytics platforms, fan engagement data systems, real-time game statistics pipelines, and athlete monitoring infrastructure

High-Tech

Product telemetry platforms, usage analytics systems, ML feature stores, and SaaS metrics infrastructure

Each industry gets compliance frameworks built-in, domain-specific data models, relevant case studies and benchmarks, and architects who speak your language.

See case studies from your industry

Clients around the world trust us

Our partners are leading technology providers

We combine our data engineering consultancy with strong tech partnership, offering you flexible access to the best-fitting solutions without lock-in

Start Small, Prove Value, Scale with Confidence

Our data engineering consulting services follow a phased approach, with clear exit ramps and transparent pricing at every stage.

Assessment

2-4 Weeks

What Happens:

We diagnose your current state, identify quick wins, quantify modernization ROI, and build a phased roadmap tailored to your team, timeline, and budget.

Deliverables

  • Current state architecture assessment
  • Gap analysis and risk identification
  • Prioritized modernization roadmap
  • Build vs. buy vs. partner recommendation
  • Detailed cost estimate for next phases

Investment: Complimentary for qualified prospects

Decision Point
No obligation to proceed. If we're not the right fit or if building internally makes more sense, we'll tell you.

Get Your Free Assessment

Pilot

8-12 Weeks

What Happens:

We deliver a working POC with defined success metrics. You get a production-ready platform, and it will be up to you to choose us as your implementation partner.

Deliverables

  • Working data pipeline for priority use case
  • Architecture documentation
  • Performance benchmarks
  • Security and compliance validation
  • Knowledge transfer sessions

Investment: Typically $150K-$300K (fixed price)

Decision Point
Pilot success metrics must be met before proceeding to Phase 3. You control the go/no-go decision.

Learn About Pilot Projects

Scale

4-9 Weeks

What Happens:

We build your production data platform in phases, with continuous knowledge transfer, regular milestone reviews, and your team learning alongside us.

Deliverables

  • Production data infrastructure
  • Automated data pipelines and orchestration
  • Governance and security framework
  • Self-service analytics capabilities
  • Team training and knowledge transfer

Investment: Typically $400K-$800K+ depending on scope and complexity

Decision Point
Milestone-based payments with phase-gate reviews. If priorities change, we adjust the roadmap. .

See Full Engagement Details

What Does This Actually Cost?

We know you need budget clarity before you can move forward. Here's what to expect:

Typical Investment Ranges

  • Pilot Projects: $150K-$300K (8-12 weeks, fixed price)
  • Full Platform Builds: $400K-$800K+ (4-9 months, milestone-based)
  • Ongoing Advisory Retainers: $15K-$30K/month (optional, not required)

What Influences Cost

  • Current infrastructure complexity
  • Data volume and velocity requirements
  • Compliance and security requirements
  • Number of data sources and systems
  • Team size and knowledge transfer needs

How We Compare

  • vs. Hiring Internally: 5 senior data engineers = $1.5M-$2M annually (if you can find and retain them)
  • vs. Big 4 Consulting Firms: 30-50% lower cost, 2-3x faster delivery
  • vs. Doing Nothing: Cost of delayed insights, competitive disadvantage, and technical debt accumulation

Bottom Line
We'll give you a detailed estimate after the assessment—with clear scope boundaries and no hidden fees.

Get Your Custom Pricing Estimate

Our Technology Stack

We build on proven, scalable technologies, not proprietary platforms that trap you. You own your architecture, not us.

Layer_1 (1)

Cloud Platforms

Built for flexibility. Optimized for performance. We architect data engineering solutions on AWS, Azure, and Google Cloud, choosing the right platform for your workload, not our preference.

  • Multi-cloud and hybrid strategies that prevent vendor lock-in
  • Cost optimization that reduces cloud spend by 30-50%
  • Cloud-agnostic designs that support future migrations

Technologies

memory

Big Data Processing

Handle massive datasets. Process at lightning speed. We leverage Apache Spark and Hadoop ecosystems to build processing systems that scale linearly with data growth.

  • Distributed processing for terabytes or petabytes
  • In-memory computing for 100x faster analytics
  • Batch and streaming workloads on unified platforms

Technologies

Layer_1 (2)

Data Warehousing

Store smarter. Query faster. Modern data warehouses that deliver sub-second query performance on billions of rows, without breaking the budget.

  • Elastic compute that scales with demand
  • Zero-maintenance infrastructure
  • Built-in optimization that adapts to query patterns

Technologies

Layer_1 (3)

Real-Time Streaming

React instantly. Process continuously. For businesses that can’t wait for batch windows, we implement streaming architectures that process data in milliseconds.

  • Low-latency ingestion for time-sensitive use cases
  • High-throughput pipelines (millions of events per second)
  • Exactly-once processing guarantees 

Technologies

Plus: dbt for transformation, Airflow for orchestration, Fivetran for ingestion, Databricks for unified analytics, Informatica for enterprise integration, Terraform for infrastructure-as-code.

Our Principle: Best tool for the job, not what's easiest for us. You get architecture designed for your requirements, not our toolkit preferences.

See our full technology capabilities

Questions Data Leaders Ask Before Engaging Our Data Engineering Consultants

That’s exactly what we help you figure out in the assessment phase. We’ll analyze your team capabilities, timeline requirements, and budget constraints, then give you an honest build-vs-buy-vs-partner recommendation. If building internally makes more sense for your situation, we’ll tell you that. We’ve turned away prospects when it was the right call.

Data engineers develop and maintain the data infrastructure that enables efficient data collection, storage, transformation, and access across an organization. They ensure reliable data pipelines and optimized systems for managing data flow.

While data analysts concentrate on analyzing data, extracting insights, and presenting findings through visualizations, data engineers are responsible for building and managing the underlying data systems and pipelines.

No, data engineering is more than just ETL. Data engineering encompasses the complete process of designing, building, and maintaining scalable data architectures. This includes data modeling, real-time processing, storage solutions, and data security.

Data engineering is crucial because it empowers businesses to transform raw data into structured, usable formats, enabling them to extract insights, make data-driven decisions, and scale efficiently. It also ensures that data remains accessible, reliable, and optimized for analysis and innovation.

In data engineering, data security and privacy are ensured through encryption, access controls, and regular audits. Secure data storage and transmission practices, along with compliance with regulations like GDPR and HIPAA, also protect sensitive data.

Data Engineering focuses on designing and building the infrastructure for data storage, processing, and access, while DataOps applies DevOps principles to automate, monitor, and improve the delivery of data pipelines and analytics.

Data engineering ensures that AI and ML models have access to clean, well-structured, and timely data by building pipelines, feature stores, and real-time processing systems. Without robust data engineering, ML projects often face delays and poor model accuracy.

A data lake is ideal for storing large volumes of raw, unstructured data for advanced analytics and machine learning, while a data warehouse is better suited for structured, query-ready data used in reporting and business intelligence. Many organizations use both in a hybrid model.

Real-time data processing uses streaming frameworks like Apache Kafka or Spark Streaming to process and deliver data instantly, enabling faster decision-making and event-driven applications.

Industries such as agriculture, finance services & insurance, healthcare, media, entertainment, gaming, sports, and high-tech see significant gains from data engineering — from risk management and predictive analytics to operational optimization and customer experience improvements.

Compliance is ensured by implementing role-based access control, encryption, data masking, audit logs, and aligning processes with standards like GDPR, HIPAA, and ISO 27001.

The ROI comes from faster insights, improved decision-making, reduced operational costs, increased automation, and enabling advanced analytics capabilities that drive revenue growth.

Yes, modern data engineering practices allow integration across AWS, Azure, GCP, and hybrid environments using tools like Fivetran, dbt, and Snowflake.

Need Help With Data
Engineering? 

We’d love to understand your goals and challenges - and help you identify opportunities to fully leverage the potential of your data journey.

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