About The Position

This role will give you the opportunity to design and shape the technical foundation of our Analytics & AI practice, working on high-impact client engagements across industries. As our Analytics & AI Architect, you will sit at the intersection of data engineering, analytics, data science, and AI — defining the standards, frameworks, and architectures that our teams build upon.

Requirements

  • Master’s degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related field.
  • Full proficiency in English + 1 additional language (French, Arabic, Spanish, German...).
  • 6+ years of technical experience in data architecture or a closely related field.
  • Proven track record in a consulting or multi-client services environment.
  • Proven hands-on experience designing large-scale data platforms: data lake, lakehouse, or warehouse architectures (Databricks, Snowflake, BigQuery, Azure Synapse, Redshift).
  • Strong command of SQL and at least one of Python, Scala, or Spark for data processing and transformation.
  • Experience with Big Data ecosystems: Hadoop, Spark, PySpark, Hive, or equivalent.
  • Familiarity with streaming and real-time architectures (Kafka, Flink, Spark Streaming).
  • Proven hands-on experience with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Azure ML, or equivalent.
  • Experience architecting MLOps pipelines: model versioning, CI/CD for ML, monitoring and drift detection.
  • Exposure to GenAI and LLM integration patterns (RAG architectures, vector databases, prompt pipelines).
  • Proven hands-on experience with orchestration and transformation tools: Airflow, dbt, or equivalent.
  • Proven hands-on experience with container technologies: Docker, Kubernetes.
  • Proven hands-on experience with versioning software: Git, GitHub, GitLab.
  • Proven hands-on experience deploying solutions in cloud ecosystems: AWS, Azure, or Google Cloud.
  • Knowledge of data governance frameworks: data catalogs, lineage tracking, access control, and data quality management.
  • Exposure to BI and data visualization platforms (Power BI, Tableau, Looker) and semantic layer design.
  • Ability to step back, analyze complex problems, define architectural options, and drive decisions.
  • Strong ability to work and collaborate with a variety of stakeholders across technical and business functions.
  • Excellent communication skills with the ability to translate complex technical architectures into clear business implications.
  • High autonomy, attention to detail, and ability to manage multiple client engagements simultaneously.

Responsibilities

  • Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment.
  • Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines.
  • Define standards for data modeling, storage, ingestion, and transformation across client engagements.
  • Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring).
  • Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling.
  • Define best practices and reusable frameworks for data engineers, analysts, and data scientists.
  • Act as a technical mentor and reviewer for cross-functional project teams.
  • Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects.
  • Contribute to internal knowledge base, toolkits, and delivery accelerators.
  • Lead architecture workshops and discovery sessions with client stakeholders.
  • Translate business requirements into scalable, robust technical blueprints.
  • Present architecture decisions to both technical teams and executive audiences.
  • Support pre-sales and proposal efforts with technical scoping and solution design.
  • Provide internal training and knowledge-sharing sessions with the team.
  • Support the Head of Practice on business development and internal capability initiatives.

Benefits

  • A competitive salary.
  • A great working environment.
  • A steep learning curve with interesting and diverse topics to work on.
  • A healthy work-life balance.
  • Health insurance benefits.
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