Senior Data Engineer

Role Overview

Responsibilities

• Build and run batch and streaming pipelines from source systems into the lake and warehouse.
• Own raw, curated, schema, quality, and lineage layers.
• Build retrieval components including connectors, document parsing, chunking, embeddings, and vector indexing.
• Create curated datasets and metrics for AI and analytics consumers.
• Add validation, monitoring, access controls, PII handling, and entitlement-aware datasets.
• Collaborate with platform and DevOps engineers on dependable data and retrieval services.
• Control storage, compute, query, embedding, and vector workload costs.
• Review code, document systems, and shape data engineering standards.

Requirements

• Eight or more years of overall data engineering experience.
• Hands-on AI or ML data engineering experience involving retrieval, embeddings, or feature data.
• Strong SQL and Python, including PySpark or similar distributed processing.
• Production experience with AWS S3, Glue, Athena, and Redshift.
• Experience with layered data architecture and raw-to-curated transformations.
• Experience with ELT or integration tools, event-driven pipelines, streaming or CDC, semantic layers, vector stores, open table formats, and orchestration.

Technical Environment

• AWS data stack: S3, Glue, Data Catalog, Athena, and Redshift.
• Possible integration tools include Airbyte, Fivetran, and Meltano.
• Possible streaming or CDC technologies include Kinesis, Amazon MSK, and Debezium.
• Possible vector stores include pgvector, Amazon OpenSearch, Pinecone, Weaviate, and Milvus.
• Possible orchestrators include Amazon MWAA, Step Functions, Dagster, and Prefect.
• Open table formats include Parquet with Apache Iceberg, Delta Lake, or Hudi.

Workplace

• The source metadata classifies the role as onsite in the Riyadh Region.