Company: Quantiphi

Location: Doha (Hybrid as per project needs)

Experience: 10-15+ years

Employment Type: Full-time

Role Overview

Quantiphi is seeking a Data & Machine Learning Architect with a strong Data Engineering and Cloud Data Architecture background, responsible for designing and building enterprise-scale data platforms and data warehouses on cloud.

This role is data engineering–led, with primary ownership of end-to-end data warehouse design, ETL/ELT pipelines, and analytics-ready data models. In addition, the architect will design and enable advanced analytics, ML, and GenAI use cases, including ML/LLM model development and productionization on cloud platforms such as GCP.

Key Responsibilities

Data Architecture & Data Engineering (Primary Focus)

• Design end-to-end data architectures covering data sourcing, ingestion, transformation, storage, and consumption.
• Build enterprise data warehouses using Bronze → Silver → Gold (medallion) architecture.
• Apply strong data warehouse modelling techniques (Kimball, Data Vault, and related methodologies).
• Architect and develop ETL / ELT pipelines using cloud-native and distributed processing frameworks.
• Work with multiple source systems, including RDBMS and diverse data formats (CSV, JSON, Parquet, Avro).
• Design scalable and governed data platforms using BigQuery, GCS, Dataplex, Cloud Composer, Dataproc, Dataflow, and Pub/Sub.
• Optimize data pipelines and warehouse performance for cost, scalability, and reliability.
• Produce high-quality technical documentation, including architecture diagrams, data flow diagrams, and data dictionaries.

Data Science, ML & GenAI (Secondary / Enablement Focus)

• Design and develop ML / LLM / NLP models to solve complex business problems on public cloud platforms (GCP preferred).
• Develop models using Python, GenAI techniques, and standard ML frameworks.
• Apply strong understanding of statistics, feature engineering, and model evaluation techniques.
• Build and productionize ML/LLM solutions, ensuring scalability, reliability, and performance.
• Implement MLOps / LLMOps practices, including:
• Model versioning
• CI/CD for ML pipelines
• Monitoring and retraining strategies
• Deploy and manage ML solutions using GCP Vertex AI.
• Collaborate closely with data engineering teams to leverage curated Gold-layer datasets for ML and advanced analytics use cases.

Note: While ML and GenAI development is part of this role, the primary expectation remains data engineering and platform architecture.

Stakeholder & Technical Leadership

• Act as a technical architect and advisor for enterprise data and ML initiatives.
• Lead architecture reviews, design workshops, and deep-dive technical discussions with clients.
• Mentor data engineers and senior technical team members.
• Support solutioning, effort estimation, and pre-sales engagements.

Required Technical Skills (Must Have)

Core Data Engineering Expertise

• Advanced SQL
• BigQuery
• Data Warehouse Modeling – Kimball, Data Vault, and related approaches
• Building end-to-end Data Warehouses (Bronze → Gold layers)
• Designing and implementing ETL / ELT pipelines

Additional Experience (Strong Plus)

• Building end-to-end data infrastructure from:
• Data sourcing
• Data ingestion
• Data transformation
• Data consumption & visualization
• Hands-on experience with:
• Dataplex
• Google Cloud Storage (GCS)