AI Data Engineer

Role and mission

The AI Data Engineer designs pipelines that acquire data from heterogeneous sources, clean and transform it, and make it available for model training and inference. The role differs from a traditional data engineer in handling AI-specific demands: large datasets, versioning, labelling and sensitive-data governance under regulatory constraints. Without good data no model functions, which is why this profile underpins the work of the AI Data Scientist and the AI Machine Learning Engineer.

Main responsibilities

• Design and implement data acquisition and transformation pipelines.
• Ensure data quality, completeness and timeliness.
• Version datasets and track transformations.
• Manage manual or automated labelling.
• Implement privacy, security and GDPR compliance.
• Monitor production pipelines and handle incidents.

Technical skills

• Advanced SQL; Python and Scala for data work
• Relational, NoSQL, data lake and data warehouse systems
• Orchestration: Apache Airflow, Dagster, Prefect; dbt, Spark, Databricks
• Streaming: Kafka, Flink, Kinesis
• Storage: Snowflake, BigQuery, Redshift; Delta Lake, Iceberg, Hudi
• Vector databases (Pinecone, Weaviate, Qdrant, Milvus, pgvector) and feature stores (Feast, Tecton)

Cross-functional skills

• Attention to detail and edge-case anticipation
• Communication with non-technical stakeholders
• Deep knowledge of GDPR and AI Act requirements
• Data governance and compliance responsibility

Training pathway and certification

Most AI Data Engineers hold a degree in computer science, engineering, statistics or the sciences, then specialise in distributed data systems and cloud infrastructure. Hands-on experience with real datasets and production pipelines matters, and continuous learning is required as the field shifts toward lakehouse, real-time and data-mesh paradigms.

Market context

Demand is high and growing — among the most sought-after roles in Italy — with cloud-native experience (Snowflake, Databricks, AWS/Azure/GCP) commanding a premium. In Italy juniors earn €32,000–€50,000, mid-level €50,000–€75,000 and seniors €75,000–€110,000, with data-platform and RAG specialists at €120,000–€140,000. Active sectors include financial services, telecommunications, retail, healthcare, energy and public administration. Increasingly the role builds RAG pipelines — ingestion, chunking, embeddings, vector indexing — for a Boston enterprise or a Gulf bank. Related UNI 11621-8 roles: AI Data Scientist and AI Machine Learning Engineer. Return to the profiles overview.

European Digital Credential by AIPIA

AIPIA is authorised by the European Commission as an issuer of European Digital Credentials (EDC) carrying the eIDAS electronic seal. The credential is cryptographically verifiable, stored in the European digital wallet and recognised across all 27 member states. Issuance follows a defined route: AIPIA membership, submission of a competency dossier (CV, training, experience and project portfolio), assessment by the technical committee against the UNI 11621-8 criteria, an optional interview, and issuance with a QR verification code. The credential is valid for three years and renewable through continuing professional development. Two further routes exist: third-party certification under ISO/IEC 17024 — for which no Italian body is yet accredited, the process being in progress — and a professional quality attestation under Article 7 of Italian Law 4/2013 for qualifying members.