"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringData Engineering for AI
Data Engineering Company Delivering AI Value
As a trusted data engineering company in USA, we build secure, scalable data platforms that turn scattered business data into AI-ready insights. Our data engineering services streamline data pipelines, cloud migration, real-time analytics, and governance to improve faster decision-making. Looking to hire data engineers? Our experts design modern architectures that integrate with AI, machine learning, and business applications, helping you reduce complexity, improve data quality, and create a stronger foundation for innovation and measurable business growth.
Data Capabilities
AI-Ready Data Engineering
Our data engineering services cover ingestion, ETL, lakehouse modeling, and feature delivery so AI teams get trustworthy data foundations that hold up in production.
Data Lakehouse Architecture
Design modern lakehouses on Databricks, Snowflake, or BigQuery with Delta Lake / Iceberg for ACID transactions and time-travel capabilities.
Real-Time Streaming Pipelines
Apache Kafka, Apache Flink, and Spark Streaming pipelines processing millions of events per second with sub-second latency for AI feature computation.
ETL/ELT Data Integration
Connect 100+ data sources — CRMs, ERPs, databases, SaaS APIs — with reliable, monitored pipelines that keep your data warehouse current.
Feature Store Engineering
Build centralized feature stores ensuring training-serving consistency, feature reuse, and point-in-time correctness for production ML models.
Data Quality & Observability
Automated data quality checks, schema validation, anomaly detection, and lineage tracking so data issues are caught before they corrupt models.
Data Governance & Security
Row-level security, column masking, data cataloging, GDPR/CCPA compliance frameworks, and access audit logging for enterprise data platforms.
Why Agile Infoways for Data Engineering
We've built data platforms processing petabytes of data for AI systems across financial services, retail, and healthcare.
Our Capability
Data Engineering Stack
Best-in-class tooling for data lakehouse architecture, governed storage, and data pipeline development across batch, streaming, and AI-ready serving workloads.
Databricks / Snowflake
Unified analytics and AI platforms with Delta Lake and automatic scaling for petabyte workloads.
Apache Kafka / Flink
Event streaming backbone for real-time data pipelines with exactly-once processing guarantees.
dbt (data build tool)
SQL-first data transformation with testing, documentation, and lineage for data warehouse layers.
Feast / Tecton
Production feature stores with offline/online serving, time-travel, and feature versioning.
Great Expectations / Monte Carlo
Data quality validation and observability with automated anomaly detection and alerting.
Airbyte / Fivetran
300+ pre-built connectors for reliable ELT with incremental syncing and change data capture.
Our ApproachHow We Build
How We Build
Data Platforms
A systematic approach from data audit through production platform with data quality at every layer.
01
Data Audit & Architecture Design
02
Pipeline Development & Integration
03
Feature Store & AI Readiness
04
Observability, Governance & Scale
Step 01
Data Audit & Architecture Design
Inventory all data sources, assess quality and latency requirements, identify AI use cases, and design the target data architecture and governance model.
Source inventoryQuality assessmentAI use-case mappingTarget architecture
Step 02
Pipeline Development & Integration
Build ingestion pipelines from all sources, implement transformation logic in dbt, set up orchestration with Airflow or Prefect, and deploy quality checks.
Ingestion pipelinesdbt transformationsOrchestration setupQuality gates
Step 03
Feature Store & AI Readiness
Implement feature engineering pipelines, deploy feature store with online/offline serving, validate point-in-time correctness, and connect to ML training.
Feature pipelinesOnline/offline servingTraining data validationML integration
Step 04
Observability, Governance & Scale
Deploy data observability tools, implement catalog and governance policies, optimize pipeline performance, and document platform for team self-service.
Data observabilityCatalog & lineagePerformance optimizationSelf-service docs
Our ApproachHow We Build
How We Build
Data Platforms
A systematic approach from data audit through production platform with data quality at every layer.
Step 01
Data Audit & Architecture Design
Inventory all data sources, assess quality and latency requirements, identify AI use cases, and design the target data architecture and governance model.
Source inventoryQuality assessmentAI use-case mappingTarget architecture
Step 02
Pipeline Development & Integration
Build ingestion pipelines from all sources, implement transformation logic in dbt, set up orchestration with Airflow or Prefect, and deploy quality checks.
Ingestion pipelinesdbt transformationsOrchestration setupQuality gates
Step 03
Feature Store & AI Readiness
Implement feature engineering pipelines, deploy feature store with online/offline serving, validate point-in-time correctness, and connect to ML training.
Feature pipelinesOnline/offline servingTraining data validationML integration
Step 04
Observability, Governance & Scale
Deploy data observability tools, implement catalog and governance policies, optimize pipeline performance, and document platform for team self-service.
Data observabilityCatalog & lineagePerformance optimizationSelf-service docs
Use Cases
Data Engineering in Production
Real data platforms powering AI systems at enterprise scale.
RE
RetailUnified Commerce Data Platform
The Challenge
Retailer with 15 data silos — POS, e-commerce, loyalty, supply chain — unable to build accurate demand forecasting models.
The Outcome
Unified lakehouse on Databricks ingesting all 15 sources, powering demand models that reduced stockouts by 35% and overstock by 28%.
DatabricksDelta LakeKafkadbt
FI
FintechReal-Time Risk Feature Platform
The Challenge
Risk models using batch features 24 hours stale — missing fraud patterns that emerged intraday.
The Outcome
Flink streaming platform computing risk features in real time, reducing fraud detection latency from 24 hours to 200ms.
Apache FlinkFeast feature storeKafkaExactly-once
HE
HealthcareHIPAA Data Lakehouse
The Challenge
Health system with patient data spread across 8 EHR systems, preventing any cross-system AI analysis.
The Outcome
HIPAA-compliant lakehouse unifying all EHR sources with PHI masking, enabling population health AI models for first time.
SnowflakeHL7 FHIRPHI maskingData governance
MA
ManufacturingIoT Sensor Data Pipeline
The Challenge
Factory with 50,000 IoT sensors generating 2TB/day with no reliable pipeline — predictive maintenance models starved of data.
The Outcome
Kafka + Spark Streaming pipeline ingesting all sensors in real time, cutting equipment downtime by 42% through predictive maintenance.
Kafka StreamsSpark Structured StreamingTime-series DBAnomaly detection
Industries
AI Built for Every Industry
Every industry re-imagined with our enterprise AI services. We are reinventing every industry and creating a unique solution that moves our clients forward and disrupts the market.
Before You Build
Planning Your Data Platform
What teams ask when building the data foundation for AI — from pipelines and ETL to lakehouses, cost, and AI-readiness.
8 questions answered
01
What is a data pipeline, and why does AI need one?
A data pipeline is an automated workflow that moves, cleans, and transforms data from source systems into a form models can use. AI needs one because models are only as good as their inputs — pipelines deliver the clean, consistent, timely data that downstream systems like RAG knowledge systems and ML models depend on for accurate results.
02
ETL vs ELT — which approach should we use?
ETL vs ELT comes down to where transformation happens. ETL transforms data before loading it into the warehouse — good for strict schemas and compliance. ELT loads raw data first and transforms it inside a modern lakehouse — faster, more flexible, and the default for AI workloads. Our data engineers pick per use case.
03
Data lakehouse vs data warehouse: which fits AI workloads?
A data lakehouse usually fits AI workloads better than a traditional data warehouse. Warehouses excel at structured BI reporting; lakehouses (Databricks, Snowflake, BigQuery with Delta or Iceberg) handle structured and unstructured data, ML feature pipelines, and ACID transactions in one place. For teams running AI, the lakehouse avoids costly data duplication — see our AI infrastructure.
04
How do we know if our data is “AI-ready”?
Data is AI-ready when it's clean, well-documented, consistently structured, and accessible through governed pipelines with known lineage and quality metrics. Gaps like missing values, duplicate records, undocumented sources, or weak freshness guarantees will drag down model performance. We usually start with an AI strategy consulting audit to identify the gaps before teams invest in delivery.
05
How much does a data engineering project cost, and how long does it take?
A production data platform typically takes 8–16 weeks, scoped in phases rather than priced flat, because cost depends on source count, data volume, real-time versus batch needs, and governance requirements. Most teams start with one high-value pipeline and expand once quality and adoption are proven. We usually frame that roadmap alongside the surrounding software and platform services it depends on.
06
How do you connect to our existing databases, warehouses, and SaaS tools?
We connect through managed connectors and change-data-capture that ingest from databases (Postgres, MySQL), warehouses (Snowflake, BigQuery), and SaaS tools (Salesforce, HubSpot) — with incremental syncs so data stays current. Structured and event data land together, governed and lineage-tracked. This same connectivity layer powers downstream AI integration so insights surface inside the tools your team already uses.
07
What's included in data engineering services and consulting?
Data engineering services and consulting usually include source assessment, architecture design, ETL and ELT planning, lakehouse modeling, orchestration, observability, governance, and rollout support. The aim is to make data usable for operations and AI, not just move it around. We often connect that foundation to custom AI development when the data platform must feed production models.
08
Do you offer ETL and data pipeline development?
Yes, we deliver both ETL services and data pipeline development for batch, streaming, and hybrid workloads. That can mean modern ELT into a lakehouse, real-time event processing, or governed movement between operational systems and analytics layers. When teams want to expose that value quickly, we also connect the platform to downstream AI solutions and business workflows.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
Book a call or message us with your project specs, and we will get back to you within 24 hours!
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