Data Engineer

Objectives of the position:

The Data Engineer is responsible for designing, building, and maintaining scalable, reliable, and secure data pipelines and platforms that power our AI-driven digital ecosystem. This role focuses on integrating data from diverse enterprise systems - including SAP, CRM, and other internal applications - into a unified data layer that enables analytics, reporting, and advanced AI/ML use cases. The Data Engineer ensures that data is accessible, high-quality, and efficiently processed to support intelligent decision-making and digital products.

Key Responsibilities:

Data Architecture & Engineering

• Design, develop, and maintain robust data pipelines (batch and streaming) integrating multiple enterprise systems and external data sources.
• Build and operate data platforms such as data lakes, lakehouses, and data warehouses to serve as the foundation for analytics and AI workloads.
• Implement Change Data Capture (CDC) patterns to enable real-time and near-real-time data synchronization from enterprise systems like SAP.
• Establish and evolve a scalable data fabric enabling seamless data integration across heterogeneous source systems (SAP, CRM, custom applications, APIs).
• Define and implement data models optimized for analytics, reporting, and downstream consumption.
• Ensure scalability, performance, and reliability of data infrastructure.

Data Integration & Processing

• Ingest, transform, and validate structured and unstructured data from enterprise systems using modern data engineering frameworks.
• Implement ETL/ELT processes with a focus on automation and reusability.
• Support master data management initiatives to ensure consistent, unified views of core business entities (customers, products, vendors) across integrated systems.
• Monitor data pipelines and proactively resolve data quality or performance issues.

Data Quality, Security & Governance

• Implement data quality checks, validation rules, and automated testing to ensure data integrity
• Build and maintain data observability capabilities including pipeline health monitoring and data lineage tracking.
• Ensure compliance with data security, privacy, and regulatory requirements (e.g., access controls, encryption, audit trails).
• Collaborate on data governance standards, metadata management, and documentation.

Collaboration & Enablement

• Work closely with data analysts, data scientists, software engineers, and business stakeholders to understand data requirements.
• Enable self-service analytics by providing reliable, well-documented datasets and interfaces.
• Support AI/ML use cases by delivering performant and trustworthy data foundations.
• Develop and maintain APIs, data models, and modular data services for downstream consumption.

Platform Operations & Optimization

• Automate deployment and operation of data infrastructure using CI/CD and Infrastructure as Code principles.
• Optimize cost, performance, and resource utilization across data platforms.
• Troubleshoot production issues and participate in on-call or support rotations if required.
• Ensure data quality, security, and availability along the entire data value chain.

Qualifications:

• Bachelor’s or master’s degree in computer science, Software Engineering, Data Engineering, or a related field.
• Minimum 3–5 years of professional data engineering experience.
• Strong proficiency in SQL and Python.
• Hands-on experience building and maintaining data pipelines using frameworks like Airflow, dbt, or similar.
• Experience with data warehousing or data lake technologies.
• Solid understanding of data modeling concepts (dimensional modeling, normalized models).
• Experience with relational databases (e.g., PostgreSQL) and familiarity with cloud data services.
• Understanding of data quality principles and testing practices.
• Familiarity with Git-based workflows and CI/CD practices.

Required Skills and Qualities:

Technical Skills

• Strong proficiency in SQL and Python for data processing and pipeline development.
• Experience with data orchestration and transformation tools (e.g., Airflow, dbt).
• Solid understanding of data warehousing concepts and dimensional modeling.
• Familiarity with data quality and testing practices.
• Experience with relational databases and basic cloud infrastructure.

Professional & Soft Skills

• Strong analytical and problem-solving skills with attention to detail.
• Ability to translate business requirements into technical data solutions.
• Effective communication skills and the ability to collaborate across technical and non-technical teams.
• Excellent communication skills in English (spoken and written); German is a plus.
• Structured, reliable working style with a focus on quality.
• Willingness to continuously learn and adapt to new data technologies.
• Team-oriented mindset with a proactive attitude.
• Ability to make effective trade-offs between technical excellence and delivery timelines.

Nice to Have

• Experience with streaming and real-time data architecture (Kafka, CDC).
• Familiarity with vector databases and embeddings for AI applications (e.g., pgvector, Pinecone).
• Exposure to ML pipelines, feature stores, or MLOps workflows.
• Knowledge of data observability tooling and practices.
• Experience with master data management concepts.
• Familiarity with data mesh or data fabric architectures.

If you are looking for a new professional challenge and opportunities for development, please send us your application documents in English (CV, certificates & degrees in one single PDF) by e-mail, stating the reference « Data Engineer - Tunisia » in the mail subject to: recrutement@ahktunis.org