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Senior Data Engineer

cander
Abu Dhabi, UAE
Full Time
Senior
Onsite
1 months ago
PythonSQLApache SparkKafkaAirflowData Lakehouse
Free

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Job Summary

  • We are seeking a Senior Data Engineer to architect and develop the core data infrastructure for AI driven transformation.
  • You will design and deploy high security Data Lakehouse environments, integrating SAP S/4HANA, Ariba, and unstructured data.
  • Lead technical implementation of 'Workstream 1: Data & Platform Foundations' for flagship projects.

Key Responsibilities

  • Design and deploy defense grade Data Lakehouse architectures as Single Source of Truth for AI driven Supply Chain Intelligence
  • Lead technical execution of Workstream 1: Data & Platform Foundations, mapping rigid enterprise systems and collaborating with cross functional teams
  • Architect and deploy ingestion pipelines for high volume transactional data from ERP systems like SAP S/4HANA, Ariba, and PLM
  • Build connectors for external market intelligence feeds (e.g., S&P Global, Orbis, EcoVadis)
  • Design and implement a standardized procurement data model and taxonomy across multiple entities
  • Engineer pipelines to ingest, process, and transform unstructured technical data into vector ready formats for RAG applications
  • Manage and optimize Vector Databases (e.g., Weaviate) to store embeddings
  • Establish data lineage and traceability protocols to link requirements to physical components
  • Implement Role Based Access Control (RBAC), audit logging, and data redaction policies
  • Deploy automated data quality frameworks to validate Bill of Materials (BOM) completeness and cost data accuracy
  • Optimize data pipelines for on premise GPU clusters and air gapped environments
  • Operate within a structured Sprint Zero environment to ensure data lineage, security, and governance meet defense grade standards

Qualifications And Experience

  • 5+ years of experience in Data Engineering, with at least 2 years focused on building pipelines for Machine Learning or Generative AI applications in an enterprise setting
  • Expert proficiency in Python, SQL, and modern data engineering frameworks including Apache Spark, Kafka, and Airflow
  • Strong experience extracting data from complex ERP environments, specifically SAP S/4HANA and SAP Ariba
  • Deep understanding of Data Lakehouse architectures (Databricks/Delta Lake), Relational Databases (PostgreSQL), and Vector Databases (Weaviate/Milvus)
  • Experience building pipelines for RAG solutions, Conversational AI agents, and classical ML models using tools such as dbt, dagster, or prefect
  • Proficiency with containerization (Docker, Kubernetes) and CI/CD pipelines for deploying data workflows in secure environments
  • Experience in Supply Chain, Manufacturing, or Defense sectors
  • Ability to navigate the governance challenges between agile data work and rigid systems engineering requirements
  • Proven ability to collaborate with Data Scientists and Backend Engineers to define data schemas

Technical Skills

  • Expert proficiency in Python and SQL for data engineering tasks
  • Strong experience with modern data engineering frameworks, including Apache Spark, Kafka, and Airflow
  • Deep expertise in extracting and processing data from complex ERP environments, specifically SAP S/4HANA and SAP Ariba
  • Comprehensive understanding of Data Lakehouse architectures, such as Databricks and Delta Lake
  • Experience with relational databases, including PostgreSQL, and vector databases like Weaviate and Milvus
  • Proven ability to develop data pipelines for Retrieval Augmented Generation (RAG) solutions, conversational agents, and classical machine learning models
  • Proficiency in containerization technologies, including Docker and Kubernetes, and experience implementing CI/CD pipelines
  • Knowledge of defense grade security protocols, including RBAC, audit logging, and data redaction policies
  • Experience building and optimizing pipelines for high performance GPU clusters and air gapped environments
  • Familiarity with automated data quality frameworks to validate critical datasets such as Bill of Materials (BOM) and cost data
  • Ability to design and implement standardized procurement data models and taxonomies
  • Experience engineering pipelines for unstructured data ingestion, including PDF tender documents, CAD metadata, and historical CONOPS

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