Data Platforms & Engineering

$187B

Data engineering services market by 2030

Mordor Intelligence

$302B

Data analytics market by 2030

Grand View Research

$48B

Data pipeline tools market by 2030

Grand View Research

25%

EBITDA uplift for data-driven organizations

McKinsey

Your data is costing you more than you think

Every enterprise has data. Few have data they can trust. The gap between collecting data and extracting value from it is where billions are lost annually through poor quality, fragmented systems, and infrastructure that wasn’t designed for the demands of AI and real-time analytics.

$12.9M

Average annual cost of poor data quality per enterprise

Organizations hemorrhage millions through duplicate records, inconsistent formats, and stale data — before they even attempt AI. Over 25% of organizations lose more than $5M annually.

Gartner / Acceldata, 2025

64%

Of organizations cite poor data quality as their biggest challenge

Nearly 2/3 of enterprises admit data quality is their primary obstacle. Worse, 67% say they don’t trust their own data which is the very foundation their AI initiatives depend on.

Precisely, State of Data Quality 2025

12x

Data engineering talent shortfall vs. demand

With 461K open positions and only 55K qualified candidates in Q2 2025, the data engineering talent gap is among the most severe in technology, making external partnerships essential.

Industry Analysis, Q2 2025

43%

Of COOs rank data quality as their most significant data priority

This isn’t just an IT problem. Operations leaders across industries recognize that data quality directly impacts revenue, customer experience, and operational efficiency.

IBM Institute for Business Value, 2025

The Oppportunity

The organizations that master their data will lead

23x

More likely to acquire customers

McKinsey Global Institute

19x

More likely to be profitable

McKinsey Global Institute

28.7%

CAGR of data analytics market to 2030

Grand View Research

20%

Performance advantage over competitors

McKinsey

03. Our approach

We advise. We build. We Stay.

AI is only as good as the data it feeds on. RedEx builds modern, scalable data platforms that ingest, clean, and govern data from across your enterprise whether it’s IoT sensors on a factory floor or transaction logs in a mainframe. We work across Snowflake, Databricks, BigQuery, Redshift, and open-source stacks because the right choice depends on your workloads, your team, and your budget.

99%

Data Accuracy

<50ms

Pipeline Latency

65%

Lower Storage Costs

Modern Data Lakehouse Architecture

Data Governance & Quality Automation

Real-Time Streaming Pipelines (Kafka/Flink)

Legacy Data Migration

01

Business-First Architecture

We start with your business questions. Every data model, pipeline, and dashboard is designed to answer the questions that drive revenue and reduce cost.

02

Quality Before Quantity

We fix your data quality before building analytics on top of it. Because the most sophisticated ML model in the world is worthless if it’s trained on bad data.

03

Built to Be Maintained

Every platform we build comes with documentation, monitoring, and knowledge transfer. Your team owns it on day one, not after a 6-month transition period.

Human. Intelligent. Measurable.

How We Help

End-to-end capabilities

From fragmented data silos to unified intelligence. We design, build, and operate data platforms that turn your most underutilized asset into your most powerful competitive advantage.

Data Platform Architecture

Design and build modern data platforms from data lakehouses to cloud-native architectures. We evaluate Snowflake, Databricks, BigQuery, and Redshift against your specific workloads, not vendor marketing.

Data Pipeline Engineering

End-to-end ETL/ELT pipeline design, development, and optimization. Real-time streaming with Kafka, batch processing with Spark, and orchestration with Airflow built for reliability at scale.

Data Governance & Quality

Implement data catalogs, lineage tracking, quality monitoring, and compliance frameworks. We make data trustworthy before you build AI on top of it because garbage in, garbage out.

Analytics & Business Intelligence

From self-service dashboards to embedded analytics. We design semantic layers, build data models, and deploy visualization platforms that turn data into decisions.

AI/ML Data Infrastructure

Build the data foundation that AI actually needs: feature stores, vector databases, training pipelines, and model serving infrastructure. Strategy-aligned, not science-project-driven.

Data Migration & Modernization

Migrate from legacy data warehouses and on-premise systems to modern cloud platforms. Zero-downtime migrations with validation frameworks that ensure nothing gets lost in translation.

Value Drivers

Where Data Platforms creates value

01

Revenue Intelligence

Unified customer data platforms that connect marketing, sales, and product data to reveal revenue patterns invisible in siloed systems. Real-time analytics that drive pricing, personalization, and market expansion decisions.

Data-driven organizations are 23x more likely to acquire customers

McKinsey Global Institute

02

Operational Efficiency

Automated data pipelines that eliminate manual reporting, reduce data preparation time by 80%, and enable real-time operational dashboards. From reactive reporting to predictive operations.

Up to 25% EBITDA increase for data-driven organizations

McKinsey

03

Risk & Compliance

Automated data lineage, quality monitoring, and compliance reporting that reduces audit preparation from weeks to hours. GDPR, CCPA, SOX, and industry-specific regulatory frameworks built into the platform.

Data governance market growing at 20.5% CAGR to $24B by 2034

Fortune Business Insights

04

AI Readiness

The single biggest factor limiting AI adoption isn’t algorithms but data quality. We build the data infrastructure that makes AI initiatives succeed instead of stalling in pilot purgatory.

Poor data quality is the #1 factor limiting AI scaling

Forrester

Analytics in Action

Intelligence that drives decisions

From real-time operational dashboards and predictive analytics to self-service BI and embedded intelligence, we build analytics platforms that people actually use.

Methodology

The DATA Framework

01

DISCOVER

Comprehensive data maturity assessment. We audit your current data landscape (sources, quality, governance gaps, technical debt) and design a target-state architecture aligned with business objectives, not technology trends.

02

architect

Engineer the data platform layer by layer. Ingestion pipelines, transformation logic, storage optimization, and semantic models, each component tested, documented, and designed for the team that will maintain it.

03

transform

Production deployment with automated quality monitoring, alerting, and self-healing pipelines. We don’t hand off a platform and walk away but we ensure it runs reliably with the right observability from day one.

04

activate

Expand the platform to new data domains, use cases, and business units. Build internal data engineering capability through knowledge transfer, documentation, and training. Your platform grows with your ambition.

The Modern Data Stack Evolution

Your data architecture should match your business maturity. We help you navigate the evolution from legacy systems to modern platforms at the pace that’s right for your organization.

foundation

Data Warehouse & ETL

Structured data, batch processing, traditional BI. The starting point for most enterprises, reliable but limited in flexibility, real-time capability, and support for unstructured data.

Technologies: SQL Server, Oracle, Teradata, Informatica

modern

Data Lakehouse & Streaming

Unified storage for structured and unstructured data. Real-time streaming, ML-ready infrastructure, and cost-effective scaling. The sweet spot for most enterprise data strategies today.

Technologies: Snowflake, Databricks, BigQuery, Kafka, dbt

advanced

Data Intelligence Platform

AI-native data infrastructure with automated governance, semantic understanding, and self-service analytics. Data products as first-class citizens with embedded quality and lineage.

Technologies: Data Mesh, Feature Stores, Vector DBs, Data Products

Evolution framework adapted from Databricks & Alation Modern Data Stack research

Tech Agnostic

We navigate the data platform landscape so you don't have to

We make IT simple for you.

The modern data stack is fragmenting fast. Dozens of competing platforms, overlapping capabilities, and the real risk of vendor lock-in. We help you build for flexibility and interoperability instead of betting on a single vendor's roadmap.