Enterprise-Grade Data Systems Delivered as a Service
Get an on-demand data engineering team without hiring. Continuous delivery, predictable cost, and enterprise-grade outcomes.
Every modern enterprise depends on data, but building in-house data engineering capability is expensive, slow, and difficult to scale.
You need teams that can:
Design architectures that evolve with your product.
Automate complex data flows across multiple platforms.
Ensure clean, consistent, and compliant data for analytics and AI.
That’s exactly what Data Engineering as a Service delivers — scalable, sprint-aligned data teams operating under one managed contract.
With Logiciel, you get AWS-, Azure-, and GCP-certified engineers who handle everything from data ingestion to governance while you focus on decisions, not deployment.
We start with a full audit of your current data landscape — sources, bottlenecks, and missed opportunities. Our architects then design a best-fit framework combining ingestion, transformation, storage, and analytics. Deliverables include:
Current → Target Architecture Map
Integration and Tooling Plan
Governance & Security Checklist
Our sprint-aligned teams implement pipelines, cloud infrastructure, and monitoring dashboards. Using best-in-class frameworks like Airflow, dbt, and Kafka, we ensure your data moves seamlessly between systems. Common Services Integrated: Our architects then design a best-fit framework combining ingestion, transformation, storage, and analytics. Deliverables include:
CRM (Salesforce, HubSpot)
ERP (NetSuite, SAP)
Data lakes (S3, BigQuery, Azure Data Lake)
BI tools (Power BI, Looker, Tableau)
We automate everything — extraction, transformation, and validation. Monitoring is baked into every workflow, giving your team full visibility into data quality, latency, and throughput. Technologies we use:
AWS Glue for managed ETL
CloudWatch & DataDog for observability
Airflow DAGs for orchestration
dbt tests for schema validation
Unlike traditional projects, DEaaS doesn’t end with delivery. We operate as a continuous data function, improving performance, reducing cloud spend, and adapting pipelines as your business evolves. Clients typically see:
40 % reduction in engineering backlog
25–50 % lower cloud costs
Airflow DAGs for orchestration
dbt tests for schema validation
| Layer | AWS Stack | GCP / Azure Stack | Use Case |
|---|---|---|---|
| Ingestion | Kinesis, Glue | Dataflow, Azure Synapse | Real-time or batch data movement |
| Processing | Lambda, EMR | DataProc, Synapse Pipelines | ETL, transformation, and enrichment |
| Storage | S3, Redshift, Lake Formation | BigQuery, Azure Data Lake | Centralized and governed data lakehouse |
| Orchestration | Airflow, Step Functions | Cloud Composer, Logic Apps | Pipeline management and scheduling |
| Analytics | QuickSight, Athena | Power BI, Looker | Visualization and insights |
| AI / ML | SageMaker | Vertex AI, Azure ML | Predictive analytics and ML pipelines |
1. Zero Setup Overhead
No need to recruit, train, or manage our teams ready to deploy in weeks.
2. Predictable Cost Structure
Flat-rate pricing per sprint or per data domain no hidden infrastructure costs.
3. Scale On Demand
Expand from 1 to 5 engineers as your data needs grow, without hiring delays.
4. Proven Frameworks, Not Experiments
Our systems are based on architectures validated across multiple clients and industries.
5. AI-First Engineering
Every build includes automation and AI integration readiness by default.
Challenge: FP&A teams overwhelmed by manual data prep and reporting.
Solution: Delivered a DEaaS engagement using AWS Glue, Lambda, and Aurora.
Result: 80 % faster analytics cycles, self-healing pipelines, and zero manual ETL maintenance.
Challenge: Disconnected marketing, CRM, and analytics data.
Solution: Logiciel’s DEaaS team built a microservices-based integration layer on GCP.
Result: 60 % faster campaign creation, $400 K+ transactions processed per campaign.
Challenge: Legacy data models limited scalability and visibility.
Solution: Built a unified AWS data backbone with automated validation and analytics pipelines.
Result: $24 M+ annual transactions, 70 % conversion rate, and cost-efficient scalability.
| Model | Ideal For | Key Benefit |
|---|---|---|
| Managed DEaaS | Continuous operations or modernization | End-to-end delivery and optimization |
| Project-Based DEaaS | One-time pipeline build, migration, or automation | Fast turnaround and predictable cost |
| Hybrid DEaaS | Co-managed delivery with in-house teams | Shared ownership, faster adoption |
| Orchestration | Airflow, Step Functions | Cloud Composer, Logic Apps |
| Analytics | QuickSight, Athena | Power BI, Looker |
| AI / ML | SageMaker | Vertex AI, Azure ML |
Faster Velocity: Sprints aligned with engineering cadence no delivery lag.
Operational Visibility: Real-time data health dashboards and cost reports.
Predictable Costs: Transparent pricing models and no vendor lock-in.
Cloud Efficiency: 20–40 % cost reduction through optimization.
AI Readiness: Every pipeline designed for ML integration from day one.
Your engineering team is overloaded with maintaining pipelines. You need faster data turnaround for analytics or product decisions. Your AWS or GCP costs are rising without clarity. You’re preparing for AI or ML rollout but lack clean, unified data.
