Data Engineering & Integration Solutions
Agility designs and builds enterprise data infrastructure, from ingestion and transformation to real-time streaming and analytics-ready warehouses.
- Data projects delivered
- 200+
- Typical implementation
- 4 to 8 wks
- Typical ETL pipeline build
- 4 to 5 wks
- Less manual data entry, insurance case study
- 95%
The short answer
What data engineering services does Agility provide?
How we build
Quality built into every pipeline
All pipelines include automated quality checks, lineage tracking, and alerting on schema drift. Clients use them to unify fragmented sources and remove manual reconciliation across systems.
Platforms include Snowflake, Databricks, dbt, Apache Kafka, Apache Spark, and AWS, Azure and GCP native services. Delivery takes 4 to 8 weeks from kick-off to a production-ready pipeline, depending on scope.
Services
What we build
Data Architecture & Infrastructure
- Data Lake Implementation: Scalable storage solutions using Azure Data Lake, Databricks Delta Lake
- Data Warehouse Design: Enterprise data warehousing with Snowflake, Azure Synapse, SQL Server
- Real-Time Streaming: Apache Kafka, Azure Event Hubs for continuous data processing
- Cloud Migration: Legacy system modernization and cloud-native data platform development
ETL/ELT Pipeline Development
- Automated Data Pipelines: Azure Data Factory, SSIS, custom Python/Spark solutions
- Data Transformation: Complex business logic implementation with validation and quality assurance
- Incremental Loading: Efficient delta processing for large-scale data synchronization
- Error Handling: Robust exception management with automated retry logic and monitoring
Enterprise Integration Solutions
- ERP System Integration: SAP, Oracle, Microsoft Dynamics automated data synchronization
- CRM Data Connectivity: Salesforce, HubSpot, Zoho real-time data flows
- API Development: RESTful APIs, GraphQL, and microservices architecture
- Legacy System Modernization: Mainframe connectivity and data bridge solutions
Data Quality & Governance
- Data Profiling: Comprehensive data quality assessment and anomaly detection
- Master Data Management: Single source of truth implementation across enterprise systems
- Data Lineage: Complete traceability from source systems through analytics platforms
- Compliance Framework: GDPR, HIPAA, SOX data governance and regulatory compliance
Technology
Which data engineering tools and platforms does Agility use?
Cloud Data Platforms
- Microsoft Azure
- Amazon AWS
- Google Cloud
- Snowflake
Big Data & Streaming
- Apache Spark
- Databricks
- Apache Kafka
- Azure Stream Analytics
Database Technologies
- SQL Server
- PostgreSQL
- MongoDB
- Redis
Integration & Development
- Python
- Power Platform
- Node.js
- Docker
Industries
Which industries use Agility data engineering services?
Healthcare & Pharmaceuticals
- Patient Data Integration (EHR, lab systems, medical devices)
- Clinical Analytics (research data aggregation and regulatory reporting)
- Population Health (public health data integration and analysis)
- Compliance Reporting (HIPAA, FDA automated audit trails)
Manufacturing & Supply Chain
- IoT Data Processing (sensor data from production equipment)
- Supply Chain Analytics (vendor, inventory, and logistics data)
- Quality Management (production data integration for quality control)
- Operational Intelligence (real-time dashboards for production monitoring)
Financial Services & Insurance
- Risk Data Aggregation (portfolio, market, and operational risk data)
- Regulatory Reporting (SOX, Basel III, Solvency II automated compliance)
- Customer 360 (unified customer data platform across all touchpoints)
- Real-Time Analytics (trading data, market feeds, and transaction processing)
Retail & E-commerce
- Omnichannel Integration (point-of-sale, e-commerce, mobile, and social data)
- Customer Analytics (behavioral data integration for personalization)
- Inventory Intelligence (real-time stock levels across channels)
- Supply Chain Visibility (vendor, logistics, and demand data integration)
Results
What results have Agility data engineering projects delivered?
Insurance Brokerage: Document Processing Pipeline
Insurance
- 500+ daily statements automatically processed from multiple carriers
- 95% reduction in manual data entry through intelligent automation
- Real-time CRM synchronization across all business operations
Manufacturing Company: Supply Chain Integration
Manufacturing
- 88% forecast accuracy through integrated demand planning data
- Real-time visibility across SAP ERP, Siemens MES, and Oracle SCM
- 40% safety stock reduction while maintaining service levels
Food Delivery Platform: Payment Data Processing
Logistics
- 1000+ delivery partners real-time payment calculation and processing
- Multiple OMS integration (Yelo, Tookan) with automated reconciliation
- 30-minute processing replacing 3-day manual workflows
How we work
How does Agility approach a data engineering project?
- 01
Data Discovery & Assessment
Comprehensive data landscape analysis, quality assessment, and integration requirements gathering
- 02
Architecture Design & Planning
Scalable data architecture design with technology selection and implementation roadmap development
- 03
Agile Development & Implementation
Iterative development approach with continuous integration and automated testing protocols
- 04
Data Quality & Validation
Rigorous data validation, quality assurance, and performance optimization before production deployment
- 05
Monitoring & Maintenance
Comprehensive monitoring, alerting, and proactive maintenance for sustained performance and reliability
Why Agility
Why do enterprises choose Agility for data engineering?
Enterprise-Grade Experience
Proven track record with complex data integration projects
Technology Leadership
Deep expertise in modern data stack technologies
Industry Specialization
Sector-specific knowledge ensuring compliance and optimization
Scalable Solutions
Future-proof architecture designed to grow with your business
FAQ
Data engineering: common questions
Everything you need to know about our data engineering implementation process.
How long does data engineering implementation take?
Our rapid implementation approach delivers data engineering solutions in 4 to 8 weeks depending on complexity. ETL pipelines: 4 to 5 weeks. Data warehouse setup: 5 to 6 weeks. Full data platform with real-time streaming: 6 to 8 weeks. Agile methods and lean teams keep delivery fast.
What makes Agility's implementation approach different?
We deploy senior data engineers directly on your project, with no layers of account managers or junior staff. Our agile squads ship production-ready data pipelines in weeks, not months, with continuous delivery and real-time collaboration. Every week you see working data flows, not presentations.
Do you provide support after deployment?
Yes, we provide post-implementation support including continuous pipeline monitoring, performance optimization, and maintenance. Our support packages ensure your data infrastructure continues delivering value with proactive updates and issue resolution.
Which cloud platforms do you support?
We have deep expertise in Azure (Data Factory, Synapse, Databricks), AWS (Glue, Redshift, EMR), and GCP (BigQuery, Dataflow). We also work with on-premise solutions like SQL Server, Oracle, and hybrid architectures combining cloud and on-premise systems.
Ready to modernize your data infrastructure?
Contact our data engineering specialists to discover how modern data architecture can speed up your analytics and business intelligence initiatives.