Data engineering, BI, data science and AI Data and AI jobs in Morocco
Data engineering, BI, data science or generative AI: drop your CV. Your tools, platforms and projects are taken assignment by assignment, and companies come to you.
Your contact details stay hidden until an approved company reaches out to you.
- Free, always
- No account, no password
- Invisible to your current employer
Recognised skills
What our analysis recognises in a data or AI CV
The skills referential holds 105 entries for this field, recognised under their usual names and abbreviations. Nothing is added that isn't in your CV; anything you add yourself is marked “declared”.
Data, BI and AI105 skills
- Apache Kafka
- Data engineering
- Big Data
- ETL
- Data warehousing
- Data lake
- Data modeling
- Data governance
- Data quality
- Master data management
- Apache Spark
- Apache Flink
- Apache Airflow
- dbt
- Talend
- Informatica PowerCenter
- IBM DataStage
- Oracle Data Integrator
- Fivetran
- Airbyte
- Apache NiFi
- Apache Hadoop
- Trino
- Apache Iceberg
- Snowflake
- Databricks
- Microsoft Fabric
- Business intelligence
- Power BI
- Tableau
- Qlik Sense
- MicroStrategy
- IBM Cognos
- Oracle Analytics (OBIEE)
- Apache Superset
- Metabase
- OLAP
- Data analysis
- Data visualization
- Data science
- Statistics
- SAS
- IBM SPSS
- Alteryx
- Dataiku
- KNIME
- Web scraping
- Artificial intelligence
- Machine learning
- Deep learning
- Generative AI
- Large language models
- Natural language processing
- Computer vision
- Time series analysis
- Chatbot development
- TensorFlow
- Keras
- XGBoost
- LangChain
- LlamaIndex
- Hugging Face
- OpenAI API
- Ollama
- MLflow
- MLOps
- Whisper
Plus 38 more specific skills (services, versions, sub-frameworks)
- PySpark
- SSIS
- SSRS
- SSAS
- Oracle GoldenGate
- Apache Hive
- Apache Impala
- Apache HBase
- Cloudera
- Delta Lake
- DAX
- Power Query
- Looker
- Looker Studio
- Pandas
- NumPy
- Matplotlib
- LLM fine-tuning
- Retrieval-augmented generation
- Prompt engineering
- AI agents
- Model Context Protocol
- OCR
- Reinforcement learning
- Recommender systems
- PyTorch
- scikit-learn
- LangGraph
- Kubeflow
- OpenCV
- YOLO
- spaCy
- NLTK
- SAP BusinessObjects
- SAP Data Services
- SAP Analytics Cloud
- Unity Catalog
- LLM evaluation
Proven or declared
A proven skill is worth more than a keyword.
A skill is “proven” when a dated experience in your CV shows you used it, and “declared” otherwise. The recruiter sees the difference, along with the sentence from your CV that serves as evidence.
- Date every assignmentStart and end month and year: the duration of each skill is calculated from your dates.
- Name the tools inside the assignmentA tool mentioned in a dated experience is proven. In a plain list at the end of the CV, it stays declared.
- At an IT services firm, name the end clientEmployer and end client are read separately: the recruiter sees who you actually worked for.
Example — fictitious data
Data engineer — A software publisher in Casablanca for a telecom operator · 01/2022 → 12/2024
Apache Airflow and Apache Spark pipelines on Databricks, data model built with dbt, Power BI dashboards for the sales management team.
Taken from this line, and proven by it:
Apache Airflow · Apache Spark · Databricks · dbt · Power BI
Matching
How your profile is matched with a job ad
For each job ad, your profile gets a score out of 100, built from six criteria. Every point gained or lost is explained to the recruiter, who makes the decision: nobody is rejected automatically.
Your role, and the neighbouring roles
Your main role is compared with the role of the job. Experience in a neighbouring role partly counts; in a related role, a little less.
- Data engineeringBusiness intelligenceneighbours
- Data engineeringData science / Machine learningneighbours
- Back-end developmentData engineeringrelated
- Data engineeringArchitecturerelated
- Cloud engineeringData engineeringrelated
- Business intelligenceData science / Machine learningrelated
Equivalent tools, not exact keywords
If the job ad asks for a tool you don't have but you master its equivalent, your experience still partly counts. A more specific skill also counts for its family, and the other way round. An exact match counts more.
- ETL toolsneighboursTalend, Informatica PowerCenter, SSIS, IBM DataStage, Oracle Data Integrator, SAP Data Services
- Pipeline orchestrationequivalentApache Airflow, Cloud Composer
- Big data processingneighboursApache Spark, Apache Flink, Apache Hadoop, Dataflow
- Lakehouse platformsneighboursDatabricks, Snowflake, Microsoft Fabric
- BI toolsneighboursPower BI, Tableau, Qlik Sense, Looker, MicroStrategy, IBM Cognos, Oracle Analytics (OBIEE), SAP BusinessObjects, SAP Analytics Cloud, Amazon QuickSight
- Deep learning frameworksequivalentPyTorch, TensorFlow, Keras
- Classical machine learningneighboursscikit-learn, XGBoost
- LLM frameworksequivalentLangChain, LlamaIndex
A broad requirement, proven by your tools
When the job ad asks for a field rather than a tool, it is proven by the specific tools in your CV:
- Data engineeringApache Spark, Apache Airflow, Databricks, dbt, Apache Kafka, Talend, Informatica PowerCenter, Azure Data Factory and more
- Big DataApache Spark, Apache Hadoop, Databricks, Apache Kafka, Apache Hive, Apache Flink
- Machine learningPyTorch, TensorFlow, Keras, scikit-learn, XGBoost, Deep learning
The six criteria of the score
- 35%SkillsThe required skills, backed by your experience, with how long and how recently you used them.
- 15%RoleYour main role compared with the job: DevOps, data, development…
- 20%ExperienceRelevant years for this job — not too few, not way too many.
- 12%SalaryYour net expectation compared with the company's budget, also net.
- 10%LocationCity, remote work, mobility: what works for both sides.
- 8%AvailabilityWhen you can start, notice period included.
Default weighting, published on the “How it works” page.
The job's must-have skills count separately: if one is missing, the whole score goes down and the profile cannot be rated “Strong”.
In data, the role matters as much as the tools: data engineering, business intelligence and data science are compared with the role of the job, and neighbouring roles partly count.
How the score works, in fullYour journey
What you do, and nothing more.
- 1
You drop your CV
PDF, Word or a photo, up to 10 MB. No account, no password. Your consent is asked before any analysis.
- 2
Your profile fills in
Experience, skills, certifications, languages, live. You remove what was misread and add what's missing.
- 3
Five questions, then a final check
Location, availability, pay as a net monthly salary or a day rate, spoken languages, French companies. Then your e-mail if it isn't on your CV, the companies that must not see you, and a summary.
- 4
Companies come to you
Your profile appears, without your name or contact details, in the rankings of the job ads that match you. An interested company contacts you by e-mail.
Your data, under your control
- Free for candidates, always.
- Your current employer, read from your CV, is hidden by default.
- Your contact details are only visible to an approved company that decides to contact you, and that access is logged.
- You can delete everything from “My profile”, immediately. Otherwise, everything is deleted 24 months after your last activity.
For companies
And on the recruiters' side?
Companies are approved by our team before they can access profiles. They paste their job ad and receive ranked, explained profiles, with the sentence from the CV that proves each skill. Name, photo, age, gender, school: none of it goes into the score.
Frequently asked questions
You're probably wondering…
I'm a BI data analyst. Am I visible for data engineering job ads?
Yes, partly: business intelligence and data engineering are neighbouring roles, as are data engineering and data science. Experience in the exact role of the job ad counts more.
The job ad asks for “machine learning”, my CV mentions PyTorch and scikit-learn. Is that enough?
Yes: a broad requirement like machine learning is proven by the specific tools in your CV, such as PyTorch, TensorFlow, Keras, scikit-learn or XGBoost.
My generative AI projects are recent. Are they taken into account?
Yes, if they are in your CV: large language models, RAG, LangChain or AI agents are among the recognised skills. Freshness counts too: when you last used each skill is calculated from your experience.
Is the salary asked as gross?
No, always net: you enter a net monthly salary in dirhams, or a day rate if you're a freelancer. Companies give their budget as net too.