Disconnected business systems
Bring approved CRM, ERP, finance, inventory or operational sources into a coherent flow without hiding their differences.
FADEL SANTIAGO / FREELANCE DATA & AI ENGINEER
Reliable data foundations for decisions and operations.
I help companies connect fragmented business systems, build tested data pipelines and analytical models, and deliver reporting that teams can use with confidence.
BUSINESS-FIRST DATA ENGINEERING
Start with the decision, report or process that is blocked, not a platform chosen in isolation.
The right starting point may be a data audit, a focused integration, a complete analytics foundation or additional engineering capacity. Architecture follows the operational need, data reality and ownership constraints.
01 / FROM FRAGMENTED DATA TO AN OPERABLE SYSTEM
Six practical delivery stages.
Each stage produces a reviewable deliverable before infrastructure and operating commitments increase.
- 01
Data and process audit
Map source systems, recurring reports, owners, definitions, manual steps, access constraints and the business decisions the data must support.
- 02
Architecture and roadmap
Define a proportionate target architecture, delivery sequence, integration boundaries, responsibilities and a path from immediate needs to maintainable foundations.
- 03
Source integration and pipelines
Connect approved business systems and files, then build observable batch or event-driven ETL/ELT flows with clear failure and recovery paths.
- 04
Modeling, quality and governance
Create shared analytical models, validation rules, lineage and ownership conventions so important metrics can be understood and checked.
- 05
Business intelligence and reporting
Translate agreed KPI definitions into useful dashboards, reporting datasets and decision-ready views for the people who operate the business.
- 06
Operations and knowledge transfer
Document runbooks, support maintenance, improve monitoring and transfer the technical and metric knowledge needed for long-term ownership.
02 / BUSINESS NEEDS
Where better data infrastructure removes friction.
The useful deliverable comes first. Tools, cloud services and Big Data patterns are selected only when the workload justifies them.
Manual recurring reporting
Replace fragile spreadsheet hand-offs with repeatable pipelines, documented transformations and reporting datasets.
Conflicting KPI definitions
Define shared measures and calculation rules so teams can trace how a dashboard number was produced.
Data quality and reconciliation
Detect missing, duplicated or inconsistent records before they reach operational or management reporting.
Warehouse, lake and Big Data foundations
Design storage and processing around actual volume, latency, access and governance needs, with room to evolve.
Focused freelance delivery support
Add data engineering capacity to an existing roadmap, migration, integration or analytics delivery team.
EXPECTED DELIVERABLES
Concrete outputs your team can review and own.
- Current-state map
- Sources, flows, report dependencies, ownership, risks and priority gaps documented in one usable view.
- Target architecture
- A proportionate design and delivery roadmap tied to operational constraints rather than vendor features.
- Pipelines and integration
- Versioned ingestion and transformation flows with explicit schedules, contracts, failures and recovery paths.
- Models and controls
- Documented analytical models, data-quality tests, lineage and governance responsibilities.
- BI and KPI layer
- Reporting datasets, metric definitions, dashboards and decision views matched to their intended users.
- Handover and support
- Runbooks, maintenance guidance, technical assistance and knowledge-transfer sessions for the agreed scope.
03 / VERIFIED TECHNICAL EVIDENCE
ORBIT shows the data stack working end to end.
ORBIT is a public portfolio demonstrator built with synthetic business data. It verifies engineering capabilities within its documented local scope; it is not presented as a commercial production deployment or client result.
DATA PLATFORM PORTFOLIO DEMONSTRATOR
ORBIT
ORBIT consolidates synthetic CRM, ERP and finance exports into PostgreSQL, transforms them with dbt, runs technical and business checks, and serves a Streamlit decision-support dashboard.
Its documented verification reports 4/4 Python tests, 10 dbt models built, 23/23 dbt tests, 5/5 business checks, zero new rows on an unchanged replay and a passing CI workflow.Open the ORBIT case study ↗Inspect the public repository ↗DOCUMENTED BOUNDARY
Verified scope and production boundary
The public implementation demonstrates ingestion, layered modeling, validation, idempotent replay, orchestration, observability and BI locally. Real deployments require company-specific connectors, KPI validation, security, access control, monitoring, backups and operating procedures.
Open the ORBIT case study ↗04 / ENGAGEMENT
Choose the route that matches the decision in front of you.
Use the existing project inquiry to describe the business problem, systems involved, reporting needs and delivery constraints. A technical scope can be shaped from there.
Data audit and architecture review
Assess sources, flows, quality, reporting dependencies and risks, then define practical priorities and a target direction.
Request a data audit ↗Complete data and BI project
Commission a defined integration, pipeline, warehouse, modeling or reporting implementation through delivery and handover.
Discuss a complete project ↗Freelance engineering mission
Add focused data engineering, analytics or BI capacity to an existing team, roadmap or transformation programme.
Discuss a freelance mission ↗WHO THIS HELPS
Engineering support for business and technical owners.
- SME owners who need dependable reporting without unnecessary platform complexity
- Operations managers working around disconnected tools and manual data hand-offs
- CTOs and technical leads planning integrations, platforms or analytics foundations
- Recruiters seeking freelance Data Engineering, Big Data or BI support in Paris and Île-de-France